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The Impact of Gender on Income Inequality

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Impact of Experience and Education on Womens Wages

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Gender Pay Discrimination in The Us Soccer

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The gender pay gap or gender wage gap is the average difference between the remuneration for men and women who are working. Women are generally considered to be paid less than men.

Differences in pay are caused by occupational segregation (with more men in higher paid industries and women in lower paid industries), vertical segregation (fewer women in senior, and hence better paying positions), ineffective equal pay legislation, women's overall paid working hours, and barriers to entry into the labor market (such as education level and single parenting rate).

The gender pay gap can be a problem from a public policy perspective because it reduces economic output and means that women are more likely to be dependent upon welfare payments, especially in old age.

The pay gap exists in nearly every profession. Mothers face an even wider pay gap than women without kids. Women with bachelor’s degrees working full time are paid 26% less than their male counterparts. Women face an income gap in retirement.

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argumentative essay on gender pay gap

Report | Wages, Incomes, and Wealth

“Women’s work” and the gender pay gap : How discrimination, societal norms, and other forces affect women’s occupational choices—and their pay

Report • By Jessica Schieder and Elise Gould • July 20, 2016

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What this report finds: Women are paid 79 cents for every dollar paid to men—despite the fact that over the last several decades millions more women have joined the workforce and made huge gains in their educational attainment. Too often it is assumed that this pay gap is not evidence of discrimination, but is instead a statistical artifact of failing to adjust for factors that could drive earnings differences between men and women. However, these factors—particularly occupational differences between women and men—are themselves often affected by gender bias. For example, by the time a woman earns her first dollar, her occupational choice is the culmination of years of education, guidance by mentors, expectations set by those who raised her, hiring practices of firms, and widespread norms and expectations about work–family balance held by employers, co-workers, and society. In other words, even though women disproportionately enter lower-paid, female-dominated occupations, this decision is shaped by discrimination, societal norms, and other forces beyond women’s control.

Why it matters, and how to fix it: The gender wage gap is real—and hurts women across the board by suppressing their earnings and making it harder to balance work and family. Serious attempts to understand the gender wage gap should not include shifting the blame to women for not earning more. Rather, these attempts should examine where our economy provides unequal opportunities for women at every point of their education, training, and career choices.

Introduction and key findings

Women are paid 79 cents for every dollar paid to men (Hegewisch and DuMonthier 2016). This is despite the fact that over the last several decades millions more women have joined the workforce and made huge gains in their educational attainment.

Critics of this widely cited statistic claim it is not solid evidence of economic discrimination against women because it is unadjusted for characteristics other than gender that can affect earnings, such as years of education, work experience, and location. Many of these skeptics contend that the gender wage gap is driven not by discrimination, but instead by voluntary choices made by men and women—particularly the choice of occupation in which they work. And occupational differences certainly do matter—occupation and industry account for about half of the overall gender wage gap (Blau and Kahn 2016).

To isolate the impact of overt gender discrimination—such as a woman being paid less than her male coworker for doing the exact same job—it is typical to adjust for such characteristics. But these adjusted statistics can radically understate the potential for gender discrimination to suppress women’s earnings. This is because gender discrimination does not occur only in employers’ pay-setting practices. It can happen at every stage leading to women’s labor market outcomes.

Take one key example: occupation of employment. While controlling for occupation does indeed reduce the measured gender wage gap, the sorting of genders into different occupations can itself be driven (at least in part) by discrimination. By the time a woman earns her first dollar, her occupational choice is the culmination of years of education, guidance by mentors, expectations set by those who raised her, hiring practices of firms, and widespread norms and expectations about work–family balance held by employers, co-workers, and society. In other words, even though women disproportionately enter lower-paid, female-dominated occupations, this decision is shaped by discrimination, societal norms, and other forces beyond women’s control.

This paper explains why gender occupational sorting is itself part of the discrimination women face, examines how this sorting is shaped by societal and economic forces, and explains that gender pay gaps are present even  within  occupations.

Key points include:

  • Gender pay gaps within occupations persist, even after accounting for years of experience, hours worked, and education.
  • Decisions women make about their occupation and career do not happen in a vacuum—they are also shaped by society.
  • The long hours required by the highest-paid occupations can make it difficult for women to succeed, since women tend to shoulder the majority of family caretaking duties.
  • Many professions dominated by women are low paid, and professions that have become female-dominated have become lower paid.

This report examines wages on an hourly basis. Technically, this is an adjusted gender wage gap measure. As opposed to weekly or annual earnings, hourly earnings ignore the fact that men work more hours on average throughout a week or year. Thus, the hourly gender wage gap is a bit smaller than the 79 percent figure cited earlier. This minor adjustment allows for a comparison of women’s and men’s wages without assuming that women, who still shoulder a disproportionate amount of responsibilities at home, would be able or willing to work as many hours as their male counterparts. Examining the hourly gender wage gap allows for a more thorough conversation about how many factors create the wage gap women experience when they cash their paychecks.

Within-occupation gender wage gaps are large—and persist after controlling for education and other factors

Those keen on downplaying the gender wage gap often claim women voluntarily choose lower pay by disproportionately going into stereotypically female professions or by seeking out lower-paid positions. But even when men and women work in the same occupation—whether as hairdressers, cosmetologists, nurses, teachers, computer engineers, mechanical engineers, or construction workers—men make more, on average, than women (CPS microdata 2011–2015).

As a thought experiment, imagine if women’s occupational distribution mirrored men’s. For example, if 2 percent of men are carpenters, suppose 2 percent of women become carpenters. What would this do to the wage gap? After controlling for differences in education and preferences for full-time work, Goldin (2014) finds that 32 percent of the gender pay gap would be closed.

However, leaving women in their current occupations and just closing the gaps between women and their male counterparts within occupations (e.g., if male and female civil engineers made the same per hour) would close 68 percent of the gap. This means examining why waiters and waitresses, for example, with the same education and work experience do not make the same amount per hour. To quote Goldin:

Another way to measure the effect of occupation is to ask what would happen to the aggregate gender gap if one equalized earnings by gender within each occupation or, instead, evened their proportions for each occupation. The answer is that equalizing earnings within each occupation matters far more than equalizing the proportions by each occupation. (Goldin 2014)

This phenomenon is not limited to low-skilled occupations, and women cannot educate themselves out of the gender wage gap (at least in terms of broad formal credentials). Indeed, women’s educational attainment outpaces men’s; 37.0 percent of women have a college or advanced degree, as compared with 32.5 percent of men (CPS ORG 2015). Furthermore, women earn less per hour at every education level, on average. As shown in Figure A , men with a college degree make more per hour than women with an advanced degree. Likewise, men with a high school degree make more per hour than women who attended college but did not graduate. Even straight out of college, women make $4 less per hour than men—a gap that has grown since 2000 (Kroeger, Cooke, and Gould 2016).

Women earn less than men at every education level : Average hourly wages, by gender and education, 2015

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The data underlying the figure.

Source :  EPI analysis of Current Population Survey Outgoing Rotation Group microdata

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Steering women to certain educational and professional career paths—as well as outright discrimination—can lead to different occupational outcomes

The gender pay gap is driven at least in part by the cumulative impact of many instances over the course of women’s lives when they are treated differently than their male peers. Girls can be steered toward gender-normative careers from a very early age. At a time when parental influence is key, parents are often more likely to expect their sons, rather than their daughters, to work in science, technology, engineering, or mathematics (STEM) fields, even when their daughters perform at the same level in mathematics (OECD 2015).

Expectations can become a self-fulfilling prophecy. A 2005 study found third-grade girls rated their math competency scores much lower than boys’, even when these girls’ performance did not lag behind that of their male counterparts (Herbert and Stipek 2005). Similarly, in states where people were more likely to say that “women [are] better suited for home” and “math is for boys,” girls were more likely to have lower math scores and higher reading scores (Pope and Sydnor 2010). While this only establishes a correlation, there is no reason to believe gender aptitude in reading and math would otherwise be related to geography. Parental expectations can impact performance by influencing their children’s self-confidence because self-confidence is associated with higher test scores (OECD 2015).

By the time young women graduate from high school and enter college, they already evaluate their career opportunities differently than young men do. Figure B shows college freshmen’s intended majors by gender. While women have increasingly gone into medical school and continue to dominate the nursing field, women are significantly less likely to arrive at college interested in engineering, computer science, or physics, as compared with their male counterparts.

Women arrive at college less interested in STEM fields as compared with their male counterparts : Intent of first-year college students to major in select STEM fields, by gender, 2014

Source:  EPI adaptation of Corbett and Hill (2015) analysis of Eagan et al. (2014)

These decisions to allow doors to lucrative job opportunities to close do not take place in a vacuum. Many factors might make it difficult for a young woman to see herself working in computer science or a similarly remunerative field. A particularly depressing example is the well-publicized evidence of sexism in the tech industry (Hewlett et al. 2008). Unfortunately, tech isn’t the only STEM field with this problem.

Young women may be discouraged from certain career paths because of industry culture. Even for women who go against the grain and pursue STEM careers, if employers in the industry foster an environment hostile to women’s participation, the share of women in these occupations will be limited. One 2008 study found that “52 percent of highly qualified females working for SET [science, technology, and engineering] companies quit their jobs, driven out by hostile work environments and extreme job pressures” (Hewlett et al. 2008). Extreme job pressures are defined as working more than 100 hours per week, needing to be available 24/7, working with or managing colleagues in multiple time zones, and feeling pressure to put in extensive face time (Hewlett et al. 2008). As compared with men, more than twice as many women engage in housework on a daily basis, and women spend twice as much time caring for other household members (BLS 2015). Because of these cultural norms, women are less likely to be able to handle these extreme work pressures. In addition, 63 percent of women in SET workplaces experience sexual harassment (Hewlett et al. 2008). To make matters worse, 51 percent abandon their SET training when they quit their job. All of these factors play a role in steering women away from highly paid occupations, particularly in STEM fields.

The long hours required for some of the highest-paid occupations are incompatible with historically gendered family responsibilities

Those seeking to downplay the gender wage gap often suggest that women who work hard enough and reach the apex of their field will see the full fruits of their labor. In reality, however, the gender wage gap is wider for those with higher earnings. Women in the top 95th percentile of the wage distribution experience a much larger gender pay gap than lower-paid women.

Again, this large gender pay gap between the highest earners is partially driven by gender bias. Harvard economist Claudia Goldin (2014) posits that high-wage firms have adopted pay-setting practices that disproportionately reward individuals who work very long and very particular hours. This means that even if men and women are equally productive per hour, individuals—disproportionately men—who are more likely to work excessive hours and be available at particular off-hours are paid more highly (Hersch and Stratton 2002; Goldin 2014; Landers, Rebitzer, and Taylor 1996).

It is clear why this disadvantages women. Social norms and expectations exert pressure on women to bear a disproportionate share of domestic work—particularly caring for children and elderly parents. This can make it particularly difficult for them (relative to their male peers) to be available at the drop of a hat on a Sunday evening after working a 60-hour week. To the extent that availability to work long and particular hours makes the difference between getting a promotion or seeing one’s career stagnate, women are disadvantaged.

And this disadvantage is reinforced in a vicious circle. Imagine a household where both members of a male–female couple have similarly demanding jobs. One partner’s career is likely to be prioritized if a grandparent is hospitalized or a child’s babysitter is sick. If the past history of employer pay-setting practices that disadvantage women has led to an already-existing gender wage gap for this couple, it can be seen as “rational” for this couple to prioritize the male’s career. This perpetuates the expectation that it always makes sense for women to shoulder the majority of domestic work, and further exacerbates the gender wage gap.

Female-dominated professions pay less, but it’s a chicken-and-egg phenomenon

Many women do go into low-paying female-dominated industries. Home health aides, for example, are much more likely to be women. But research suggests that women are making a logical choice, given existing constraints . This is because they will likely not see a significant pay boost if they try to buck convention and enter male-dominated occupations. Exceptions certainly exist, particularly in the civil service or in unionized workplaces (Anderson, Hegewisch, and Hayes 2015). However, if women in female-dominated occupations were to go into male-dominated occupations, they would often have similar or lower expected wages as compared with their female counterparts in female-dominated occupations (Pitts 2002). Thus, many women going into female-dominated occupations are actually situating themselves to earn higher wages. These choices thereby maximize their wages (Pitts 2002). This holds true for all categories of women except for the most educated, who are more likely to earn more in a male profession than a female profession. There is also evidence that if it becomes more lucrative for women to move into male-dominated professions, women will do exactly this (Pitts 2002). In short, occupational choice is heavily influenced by existing constraints based on gender and pay-setting across occupations.

To make matters worse, when women increasingly enter a field, the average pay in that field tends to decline, relative to other fields. Levanon, England, and Allison (2009) found that when more women entered an industry, the relative pay of that industry 10 years later was lower. Specifically, they found evidence of devaluation—meaning the proportion of women in an occupation impacts the pay for that industry because work done by women is devalued.

Computer programming is an example of a field that has shifted from being a very mixed profession, often associated with secretarial work in the past, to being a lucrative, male-dominated profession (Miller 2016; Oldenziel 1999). While computer programming has evolved into a more technically demanding occupation in recent decades, there is no skills-based reason why the field needed to become such a male-dominated profession. When men flooded the field, pay went up. In contrast, when women became park rangers, pay in that field went down (Miller 2016).

Further compounding this problem is that many professions where pay is set too low by market forces, but which clearly provide enormous social benefits when done well, are female-dominated. Key examples range from home health workers who care for seniors, to teachers and child care workers who educate today’s children. If closing gender pay differences can help boost pay and professionalism in these key sectors, it would be a huge win for the economy and society.

The gender wage gap is real—and hurts women across the board. Too often it is assumed that this gap is not evidence of discrimination, but is instead a statistical artifact of failing to adjust for factors that could drive earnings differences between men and women. However, these factors—particularly occupational differences between women and men—are themselves affected by gender bias. Serious attempts to understand the gender wage gap should not include shifting the blame to women for not earning more. Rather, these attempts should examine where our economy provides unequal opportunities for women at every point of their education, training, and career choices.

— This paper was made possible by a grant from the Peter G. Peterson Foundation. The statements made and views expressed are solely the responsibility of the authors.

— The authors wish to thank Josh Bivens, Barbara Gault, and Heidi Hartman for their helpful comments.

About the authors

Jessica Schieder joined EPI in 2015. As a research assistant, she supports the research of EPI’s economists on topics such as the labor market, wage trends, executive compensation, and inequality. Prior to joining EPI, Jessica worked at the Center for Effective Government (formerly OMB Watch) as a revenue and spending policies analyst, where she examined how budget and tax policy decisions impact working families. She holds a bachelor’s degree in international political economy from Georgetown University.

Elise Gould , senior economist, joined EPI in 2003. Her research areas include wages, poverty, economic mobility, and health care. She is a co-author of The State of Working America, 12th Edition . In the past, she has authored a chapter on health in The State of Working America 2008/09; co-authored a book on health insurance coverage in retirement; published in venues such as The Chronicle of Higher Education ,  Challenge Magazine , and Tax Notes; and written for academic journals including Health Economics , Health Affairs, Journal of Aging and Social Policy, Risk Management & Insurance Review, Environmental Health Perspectives , and International Journal of Health Services . She holds a master’s in public affairs from the University of Texas at Austin and a Ph.D. in economics from the University of Wisconsin at Madison.

Anderson, Julie, Ariane Hegewisch, and Jeff Hayes 2015. The Union Advantage for Women . Institute for Women’s Policy Research.

Blau, Francine D., and Lawrence M. Kahn 2016. The Gender Wage Gap: Extent, Trends, and Explanations . National Bureau of Economic Research, Working Paper No. 21913.

Bureau of Labor Statistics (BLS). 2015. American Time Use Survey public data series. U.S. Census Bureau.

Corbett, Christianne, and Catherine Hill. 2015. Solving the Equation: The Variables for Women’s Success in Engineering and Computing . American Association of University Women (AAUW).

Current Population Survey Outgoing Rotation Group microdata (CPS ORG). 2011–2015. Survey conducted by the Bureau of the Census for the Bureau of Labor Statistics [ machine-readable microdata file ]. U.S. Census Bureau.

Goldin, Claudia. 2014. “ A Grand Gender Convergence: Its Last Chapter .” American Economic Review, vol. 104, no. 4, 1091–1119.

Hegewisch, Ariane, and Asha DuMonthier. 2016. The Gender Wage Gap: 2015; Earnings Differences by Race and Ethnicity . Institute for Women’s Policy Research.

Herbert, Jennifer, and Deborah Stipek. 2005. “The Emergence of Gender Difference in Children’s Perceptions of Their Academic Competence.” Journal of Applied Developmental Psychology , vol. 26, no. 3, 276–295.

Hersch, Joni, and Leslie S. Stratton. 2002. “ Housework and Wages .” The Journal of Human Resources , vol. 37, no. 1, 217–229.

Hewlett, Sylvia Ann, Carolyn Buck Luce, Lisa J. Servon, Laura Sherbin, Peggy Shiller, Eytan Sosnovich, and Karen Sumberg. 2008. The Athena Factor: Reversing the Brain Drain in Science, Engineering, and Technology . Harvard Business Review.

Kroeger, Teresa, Tanyell Cooke, and Elise Gould. 2016.  The Class of 2016: The Labor Market Is Still Far from Ideal for Young Graduates . Economic Policy Institute.

Landers, Renee M., James B. Rebitzer, and Lowell J. Taylor. 1996. “ Rat Race Redux: Adverse Selection in the Determination of Work Hours in Law Firms .” American Economic Review , vol. 86, no. 3, 329–348.

Levanon, Asaf, Paula England, and Paul Allison. 2009. “Occupational Feminization and Pay: Assessing Causal Dynamics Using 1950-2000 U.S. Census Data.” Social Forces, vol. 88, no. 2, 865–892.

Miller, Claire Cain. 2016. “As Women Take Over a Male-Dominated Field, the Pay Drops.” New York Times , March 18.

Oldenziel, Ruth. 1999. Making Technology Masculine: Men, Women, and Modern Machines in America, 1870-1945 . Amsterdam: Amsterdam University Press.

Organisation for Economic Co-operation and Development (OECD). 2015. The ABC of Gender Equality in Education: Aptitude, Behavior, Confidence .

Pitts, Melissa M. 2002. Why Choose Women’s Work If It Pays Less? A Structural Model of Occupational Choice. Federal Reserve Bank of Atlanta, Working Paper 2002-30.

Pope, Devin G., and Justin R. Sydnor. 2010. “ Geographic Variation in the Gender Differences in Test Scores .” Journal of Economic Perspectives , vol. 24, no. 2, 95–108.

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Guest Essay

The Gender Gap Is Taking Us to Unexpected Places

argumentative essay on gender pay gap

By Thomas B. Edsall

Mr. Edsall contributes a weekly column from Washington, D.C., on politics, demographics and inequality.

In one of the most revealing studies in recent years, a 2016 survey of 137,456 full-time, first-year students at 184 colleges and universities in the United States, the U.C.L.A. Higher Education Research Institute found “the largest-ever gender gap in terms of political leanings: 41.1 percent of women, an all-time high, identified themselves as liberal or far left, compared to 28.9 percent of men.”

The institute has conducted freshmen surveys every year since 1966. In the early days, until 1980, men were consistently more liberal than women. In the early and mid-1980s, the share of liberals among male and female students was roughly equal, but since 1987, women have been more liberal than men in the first year of college.

While liberal and left identification among female students reached a high in 2016, male students remained far below their 1971 high, which was 44 percent.

Along parallel lines, a Knight Foundation survey in 2017 of 3,014 college students asked: “If you had to choose, which do you think is more important, a diverse and inclusive society or protecting free speech rights.”

Male students preferred protecting free speech over an inclusive and diverse society by a decisive 61 to 39. Female students took the opposite position, favoring an inclusive, diverse society over free speech by 64 to 35.

Majorities of both male and female college students in the Knight survey support the view that the First Amendment should not be used to protect hate speech, but the men were more equivocal, at 56 to 43, than women, at 71 to 29.

The data on college students reflects trends in the electorate at large. The Pew Research Center provided The Times with survey data showing that among all voters, Democrats are 56 percent female and 42 percent male, while Republicans are 52 percent male and 48 percent female, for a combined gender gap of 18 points. Pew found identical gender splits among voters who identify as liberal and those who identify as conservative.

“Significant gender differences in party identification have been evident since the early 1980s,” according to the Rutgers Center for American Women and Politics , which provides data on the partisanship of men and women from 1952 to the present day.

It’s clear from all this that the political engagement of women is having a major impact on the social order, often in ways that are not fully understood.

Take the argument made in the 2018 paper “ The Suffragist Peace ” by Joslyn N. Barnhart of the University of California-Santa Barbara, Allan Dafoe at the Center for the Governance of AI , Elizabeth N. Saunders of Georgetown and Robert F. Trager of U.C.L.A.:

Preferences for conflict and cooperation are systematically different for men and women. At each stage of the escalatory ladder, women prefer more peaceful options. They are less apt to approve of the use of force and the striking of hard bargains internationally, and more apt to approve of substantial concessions to preserve peace. They impose higher audience costs because they are more approving of leaders who simply remain out of conflicts, but they are also more willing to see their leaders back down than engage in wars.

The increasing incorporation of women into “political decision-making over the last century,” Barnhart and her co-authors write, raises “the question of whether these changes have had effects on the conflict behavior of nations.”

Their answer: “We find that the evidence is consistent with the view that the increasing enfranchisement of women, not merely the rise of democracy itself, is the cause of the democratic peace.”

Put another way, “the divergent preferences of the sexes translate into a pacifying effect when women’s influence on national politics grows” and “suffrage plays a direct and important role in generating more peaceful interstate relations by altering the political calculus of democratic leaders.”

Barnhart added by email:

The important thing to remember here is that with any trait, we are talking about averages and distributions and not categorical distinctions. Some men will have lesser preference for the use of force than some women and vice versa. The distribution of traits among the two genders overlaps. So we shouldn’t expect perfect partisan distinction.

Other consequential shifts emerged as women’s views began to change and they became more involved in politics.

Dennis Chong , a political scientist at the University of Southern California, wrote by email that “a gender gap in political tolerance, with women being somewhat more willing to censor controversial and potentially harmful ideas, goes back to the earliest survey research on the subject in the 1950s.”

There are a number of possible explanations, Chong said, including “stronger religious and moral attitudes among women; lesser political involvement resulting in weaker support for democratic norms; social psychological factors such as intolerance of ambiguity and uncertainty which translate to intolerance for political and social nonconformity; and greater susceptibility to feelings of threats posed by unconventional ideas and groups.”

Studies using moral foundations theory , Chong continued, have

found broad value differences between men and women. Women score higher on values defined by care, fairness, benevolence, and protecting the welfare of others, reflecting greater empathy and preference for cooperative social relations. In today’s debates over free speech and cancel culture, these social psychological and value differences between men and women are in line with surveys showing that women are more likely than men to regard hate speech as a form of violence rather than expression, to support laws against divisive hate speech, and to be skeptical that the right to free speech protects the disadvantaged more than the majority.

In addition, Chong said, “Women are also more likely than men to believe that colleges ought to protect students from exposure to controversial speakers whose ideas may create an inhospitable learning environment.”

Steven Pinker , a professor of psychology at Harvard, writes in his book “ The Better Angels of Our Nature ,” that “the most fundamental empirical generalization about violence” is that

it is mainly committed by men. From the time they are boys, males play more violently than females, fantasize more about violence, consume more violent entertainment, commit the lion’s share of violent crimes, take more delight in punishment and revenge, take more foolish risks in aggressive attacks, vote for more warlike policies and leaders, and plan and carry out almost all the wars and genocides.

Pinker continues:

Feminization need not consist of women literally wielding more power in decisions on whether to go to war. It can also consist in a society moving away from a culture of manly honor, with its approval of violent retaliation for insults, toughening of boys through physical punishment, and veneration of martial glory.

In an email, Pinker wrote:

We’re seeing two sets of forces that can pull in opposite directions. One set comprises the common interests of men on the one hand and women on the other. Men tend to be more obsessed with status and dominance and are more willing to take risks to compete for them; women are more likely to prize health and safety and to reduce conflict. The ultimate (evolutionary) explanation is that for much of human prehistory and history successful men and coalitions of men potentially could multiply their mates and offspring, who had some chance of surviving even if they were killed, whereas women’s lifetime reproduction was always capped by the required investment in pregnancy and nursing, and motherless children did not survive.

“ Mapping the Moral Domain ,” a 2011 paper by Jesse Graham , a professor of management at the University of Utah, and five colleagues, found key differences between the values of men and women, especially in the case of the emphasis women place on preventing harm , especially harm to the marginalized and those least equipped to protect themselves.

I asked Jonathan Haidt , a social psychologist at N.Y.U.’s Stern School of Business, about the changing political role of women. He emailed back:

In general, when looking at sex differences in outcomes, it is helpful to remember that differences between men and women on values and cognitive abilities are generally small, while differences between men and women in the activities that interest them, and in their relational styles (especially involving conflict) are often large.

When the academic world opened up to women in the 1970s and 1980s, Haidt continued, “women flooded into some areas but showed less interest in others. In my experience, having entered in the 1990s, the academic culture of predominantly female fields is very different from those that are predominantly male.”

Haidt noted:

Boys and men enjoy direct status competition and confrontation, so the central drama of male-culture disciplines is ‘“Hey, Jones says his theory is better than Smith’s; let’s all gather around and watch them fight it out, in a colloquium or in dueling journal articles.” In fact, I’d say that many of the norms and institutions of the Anglo-American university were originally designed to harness male status-seeking and turn it into scholarly progress.

Women are just as competitive as men, Haidt wrote, “but they do it differently.”

He cited a 2013 paper, “ The development of human female competition: allies and adversaries ,” by Joyce Benenson , of Harvard’s department of human evolutionary biology. In it, Benenson writes:

From early childhood onwards, girls compete using strategies that minimize the risk of retaliation and reduce the strength of other girls. Girls’ competitive strategies include avoiding direct interference with another girl’s goals, disguising competition, competing overtly only from a position of high status in the community, enforcing equality within the female community and socially excluding other girls.

In summary, Benenson wrote:

From early childhood through old age, human females’ reproductive success depends on provisioning, protecting and nurturing first younger siblings, then their own children and grandchildren. To safeguard their health over a lifetime, girls use competitive strategies that reduce the probability of physical retaliation, including avoiding direct interference with another girl’s goals and disguising their striving for physical resources, alliances and status.

In a November 2021 paper, “ Self-Protection as an Adaptive Female Strategy ,” Benenson, Christine E. Webb and Richard W. Wrangham , all of the department of human evolutionary biology, report that they

found consistent support for females’ responding with greater self-protectiveness than males. Females mount stronger immune responses to many pathogens; experience a lower threshold to detect, and lesser tolerance of, pain; awaken more frequently at night; express greater concern about physically dangerous stimuli; exert more effort to avoid social conflicts; exhibit a personality style more focused on life’s dangers; react to threats with greater fear, disgust and sadness; and develop more threat-based clinical conditions than males.

These differences manifest in a number of behaviors and characteristics, Benenson, Webb and Wrangham argue:

We found that females exhibited stronger self-protective reactions than males to important biological and social threats; a personality style more geared to threats; stronger emotional responses to threat; and more threat-related clinical conditions suggestive of heightened self-protectiveness. That females expressed more effective mechanisms for self-protection is consistent with females’ lower mortality and greater investment in child care compared with males.” In addition, “females more than males exhibit a lower threshold for detecting many sensory stimuli; remain closer to home; overestimate the speed of incoming stimuli; discuss threats and vulnerabilities more frequently; find punishment more aversive; demonstrate higher effortful control and experience deeper empathy; express greater concern over friends’ and romantic partners' loyalty; and seek more frequent help.

In an email, Benenson added another dimension to the discussion of sex roles in organizational politics:

From an early age, women clearly dislike group hierarchies of same-sex individuals more than men do. Thus, while boys and men are more willing to compete directly with both higher and lower status individuals, girls and women prefer to interact with same-sex individuals of similar status. This does not mean however that girls and women don’t care about status as much as boys and men do. For both sexes, high status increases the probability that one lives longer and so do one’s children. The result of these two somewhat conflicting motives is that girls and women seek high status but disguise this quest by avoiding direct contests. This gender difference likely impacts how women seek to shape organizational culture.

The strategies Benenson and her colleagues describe, Haidt pointed out,

lead to a different kind of conflict. There is a greater emphasis on what someone said which hurt someone else, even if unintentionally. There is a greater tendency to respond to an offense by mobilizing social resources to ostracize the alleged offender.

In “ Feminist and Anti-Feminist Identification in the 21st Century United States ,” Laurel Elder , Steven Greene and Mary-Kate Lizotte , political scientists at Hartwick College, North Carolina State University and Augusta University, analyzed the responses of those who identified themselves as feminists or anti-feminists in 1992 and 2016.

Based on surveys conducted by American National Election Studies, Elder, Greene and Lizotte found that the total number of voters saying that they were feminists grew from 28 percent to 34 percent over that period. The growth was larger among women, 29 percent to 50 percent, than among men, 18 percent to 25 percent.

Some of the biggest gains were among the young, 18-to-24-year-olds, doubling from 21 percent to 42 percent. Most striking is the data revealing the antithetical trends between women with college degrees, whose self-identification as feminist rose from 34 percent to 61 percent, in contrast to men with college degrees, whose self-identification as feminist fell from 37 percent to 35 percent.

Anti-feminist identity, the authors found,

is not just a mirror image of feminist identity but its own distinctive social identity. A striking difference between feminist and anti-feminist identification is that while gender is a huge driver in feminist identification in 2016, there is essentially no gender gap among anti-feminists. Indeed, bivariate analysis shows that 16 percent of women and 17 percent of men identify as anti-feminists.

In addition, Elder, Greene and Lizotte wrote, “while young people were more likely to identify as feminists than older generations in 2016, young people, particularly young women, also have a higher level of anti-feminist identification compared to older groups.”

The other patterns of anti-feminist identification, according to the authors, are “more the mirror image of feminist identification” with “Republicans being more likely to identify as anti-feminists compared to Democrats, and stay-at-home parents/homemakers, those who identify as born again, and those who attend church frequently being more anti-feminist.”

To provoke further discussion, I will end with the argumentative economist Tyler Cowen , of George Mason University and “ Marginal Revolution .” In December 2019, Cowen wrote a column for Bloomberg, “ Women Dominated the Decade ,” subtitled “The 2010s were pretty thrilling if you liked music, books, TV or movies by or about women.”

Cowen, who acknowledges describing “feminization in not entirely glowing terms ” — indeed one would have to say hostile terms — is also, in other contexts, unequivocally enthusiastic about “what I see as the No. 1. trend of the decade: the increasing influence of women.”

“I had the best of both worlds,” Cowen writes, “namely to grow up in the ‘tougher’ society, but live most of my life in the more feminized society.”

The Times is committed to publishing a diversity of letters to the editor. We’d like to hear what you think about this or any of our articles. Here are some tips . And here's our email: [email protected] .

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Because of an editing error, an earlier version of this article misstated the percentage of women in a Knight Foundation survey who said the First Amendment should not be used to protect hate speech. It was 71 percent, not 7 percent.

How we handle corrections

Thomas B. Edsall has been a contributor to the Times Opinion section since 2011. His column on strategic and demographic trends in American politics appears every Wednesday. He previously covered politics for The Washington Post. @ edsall

UN Women Strategic Plan 2022-2025

Everything you need to know about pushing for pay equity

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illustration of women waving an equal pay banner

Workers worldwide look forward to payday. But while a paycheck may bring a sense of relief, satisfaction, or joy, it can also represent an injustice—a stark reminder of persistent inequalities between men and women in the workplace.

The gender pay gap stands at 20 per cent , meaning women workers earn 80 per cent of what men do. For women of colour, migrant women, those with disabilities, and women with children, the gap is even greater.

The cumulative effect of pay disparities has real, daily negative consequences for women, their families, and society, especially during crises. The widespread effects of COVID-19 have plunged up to 95 million people into extreme poverty, with one in every 10 women globally living in extreme poverty . If current trends continue, 342.4 million women and girls will be living on less than $2.15 a day by 2030.

What do we mean by equal pay for work of equal value?

Equal pay for work of equal value, as defined by the ILO Equal Remuneration Convention , means that all workers are entitled to receive equal remuneration not only for identical tasks but also for different work considered of equal value. This distinction is crucial because jobs held by women and men may involve varying qualifications, skills, responsibilities, or working conditions, yet hold equal value and warrant equal pay.

In 2020, New Zealand passed the Equal Pay Amendment Bill , ensuring that women and men are paid equally for work that’s different but has equal value, including in chronically underpaid female-dominated industries. 

It is also important to recognize that remuneration is more than a basic wage; it encompasses all the elements of earnings. This includes overtime pay, bonuses, travel allowances, company shares, insurance, and other benefits.

Why does the gender pay gap persist?

The gender pay gap originates from ingrained inequalities. Women, particularly migrant women, are overrepresented in the informal sector. Look around you, from street vending to domestic service, from coffee shop attendants to subsistence farming. Women fill informal jobs that often fall outside the domains of labour laws, trapping them in low-paying, unsafe working environments, without social benefits. These poor conditions for women workers perpetuate the gender pay gap.

Women also do  three more hours of daily care work  than men , globally. This includes household tasks such as cooking, cleaning, fetching firewood and water, and taking care of children and the elderly. Although care work is the backbone of thriving families, communities, and economies, it remains undervalued and underrecognized. Try calculating your daily load with  UN Women’s unpaid care calculator .

The  motherhood penalty exacerbates pay inequity, with working mothers facing lower wages, a disparity that jumps as the number of children a woman has increases. Lower wages for mothers are linked to reduced working time, employment in more family-friendly jobs that tend to be lower paying, hiring and promotion decisions that penalize the careers of mothers, and a lack of programmes to support women’s return to work after time out of the labour market.

Restrictive, traditional gender roles are also spurring pay inequalities. Gender stereotypes steer women away from occupations traditionally dominated by men and push them toward care-focused work that is often regarded as “unskilled,” or “soft-skilled” and therefore, lower paid.

Furthermore, discriminatory hiring practices and promotion decisions that prevent women from gaining leadership roles and highly paid positions sustain the gender pay gap.

Why is pay equity an urgent issue?

Pay equity matters because it is a glaring injustice and subjects millions of women and families to lives of entrenched poverty and opportunity gaps. At the current rate, we risk leaving more than 340 million women and girls in abject poverty by 2030 , and an alarming 4 per cent could grapple with extreme food insecurity by that year.

Women also experience significantly lower social protection coverage than men, a discrepancy that largely reflects and reproduces their lower labour force participation rates, higher levels of temporary and precarious work, and informal employment. All these factors contribute to lower income , savings, and pensions of women and gendered poverty in old age.

What should be done?

As more women are plunged into poverty, the fight for equal pay and pay equity takes on a new sense of urgency because those who earn the least are most damaged by income discrepancy.

In the United States, Black women earn only 63.7 cents , Native women 59 cents , and Latinas 57 cents for every dollar that white men earn. Where money is tight, lower pay can prevent women and families from putting food on the table, securing safe housing, and accessing critical medical care and education—impacts that can perpetuate cycles of poverty across generations.

It is urgent that we put female workers on equal footing as male workers. In a world on the brink of a looming care deficit,  women make up 67 per cent of workers providing essential health and social care services globally . Governments must address underpaid and undervalued jobs in the care sector, including in education, health care and social services, all jobs that women predominantly occupy.

What does the data say about pay equity around the world? 

Unequal pay is a stubborn and universal problem. Despite significant progress in women’s education and labour market participation, progress in closing the gender pay gap has been too slow. At this pace, it will take  almost 300 years to achieve economic gender parity .

Women workers’ average pay is generally lower than men’s in all countries and for all levels of education, and age groups, with women earning on average 80 per cent what men ear n. Women in male-dominated industries may earn more than those in female-dominated industries, but the gender pay gap persists across all sectors.

While gender pay gap estimates can vary substantially across regions and even within countries, higher income countries tend to have lower levels of wage inequality compared to low and middle-income countries. However, estimates of the gender pay gap understate the real extent of the issue, particularly in developing countries, because of a lack of information about informal economies, which are disproportionately made up of women workers, so the full picture is likely worse than what the available data shows us.

Explore  UN Women’s report on the gender pay gap in Eastern and Southern Africa .

Closing the gender pay gap requires a set of measures that push for decent work for all people. This includes measures that promote the formalization of the informal economy, bringing informal workers under the umbrella of legal and effective protection and empowering them to better defend their interests.

Ensuring workers’ right to organize and bargain collectively is an important part of the solution. Women must be involved in employer and union leadership, enabling legislation that establishes comprehensive frameworks for gender equality in the workplace.

Economic empowerment Chief at UN Women Dr. Jemimah Njuki says that, “The gender pay gap requires all stakeholders, including employers, governments, trade unions take full responsibility and work side by side to address these challenges. Women deserve equal pay for work of equal value”.

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Ronald E. Riggio Ph.D.

Why the Gender Pay Gap Still Exists

Are today’s working women leaning in.

Posted August 23, 2023 | Reviewed by Lybi Ma

  • The gender pay gap exists, women make less than men. One belief is women don’t negotiate for themselves.
  • A new series of recently published studies suggests that the belief that women don’t lean in is wrong.
  • What factors account for the pervasive gender gap in pay? Front and center is bias and discrimination.

Is there still a gender pay gap? The Pew Research Center estimates that women earn an average of 82 percent of what men are paid for comparable work. The pay gap between what men and women make is real. What are the reasons?

One belief is that men tend to get paid more because they are more likely than women to promote themselves and negotiate for higher pay. This idea that women, compared to men, don’t lean in and advocate for themselves was the topic of popular books by former Facebook COO, Sheryl Sandberg (2013), and Women Don’t Ask: Negotiation and the Gender Divide (Babcock and Laschever, 2003). A new series of studies published in the Academy of Management Discoveries (Kray, Kennedy, and Lee, 2023) suggests that the stereotype that women don’t lean in and negotiate their salaries is wrong.

In this series of studies, women and men, both from the general population, as well as graduates with MBA degrees were asked how much they tried to negotiate higher initial salaries, and how much they asked for raises and promotions later in their careers. The results suggested that women actually engaged in more negotiation than men. Yet, analyses of salaries and career trajectories over time suggested that women were paid less than men (the well-known gender pay gap) and that they were more likely to be turned down for raises and promotions.

Moreover, when people were asked if they believed that part of the gender gap in wages was due to women not negotiating, a significant number of men, and women, believed that it was true (even though the research results debunked the women not leaning in stereotype). Interestingly, men, as opposed to women, were more likely to believe that women’s lack of negotiating led to the pay gap.

If the Gender Pay Gap Is Not Due to Women’s Lack of Negotiation, Why Does It Still Exist?

There may be some other reasons. Typically, women have greater responsibility for household duties, and women are more likely than men to take time out of their career progression to have and raise children. There is also some evidence that women may choose less lucrative career paths, in sectors that tend to be lower paying (for example, education and healthcare). However, the results of these new studies, and earlier research, suggest that simple discrimination and bias against women in the workforce is a primary reason.

What Are Some of the Reasons for Bias?

In positions of leadership , there is still a tendency to view the prototypical leader as a man, and one who has stereotypically masculine, agentic qualities, such as assertiveness , competitiveness, and dominance. Women, as a group, are less agentic and more communal – helpful, nurturing, and kind. In selecting leaders, there is a preference for more agentic qualities, and there is, in many organizations, a preference for a strongman leader.

One psychological reason that may both explain the false belief that women don’t lean in and negotiate for themselves, and may underpin continued gender discrimination in employment is the tendency toward blaming the victim. To rationalize why a pay gap exists, many employers may turn to the false beliefs that women don’t negotiate or stand up for themselves, that women will fall off of their career paths to raise children, or that women aren’t as competitive and high-achieving as men.

In any case, this research demonstrates that the gender pay gap is not because women don’t lean in!

Kray, L., Kennedy, J., & Lee, M. (2023). Now, Women Do Ask: A Call to Update Beliefs about the Gender Pay Gap. Academy of Management Discoveries , (ja).

Sandberg, S. & Scovell, N. (2013). Lean In: Women, Work and the Will to Lead. Knopf.

Babcock, L., & Laschever, S. (2003). Women don't ask: Negotiation and the gender divide . Princeton University Press.

Ronald E. Riggio Ph.D.

Ronald E. Riggio, Ph.D. , is the Henry R. Kravis Professor of Leadership and Organizational Psychology at Claremont McKenna College.

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The persistence of pay inequality: The gender pay gap in an anonymous online labor market

Leib litman.

1 Department of Psychology, Lander College, Flushing, New York, United States of America

Jonathan Robinson

2 Department of Computer Science, Lander College, Flushing, New York, United States of America

3 Department of Health Policy & Management, Mailman School of Public Health, Columbia University, New York, New York, United States of America

Cheskie Rosenzweig

4 Department of Clinical Psychology, Columbia University, New York, New York, United States of America

Joshua Waxman

5 Department of Computer Science, Stern College for Women, New York, New York, United States of America

Lisa M. Bates

6 Department of Epidemiology, Mailman School of Public Health, Columbia University New York, New York, United States of America

Associated Data

Due to the sensitive nature of some of the data, and the terms of service of the websites used during data collection (including CloudResearch and MTurk), CloudResearch cannot release the full data set to make it publically available. The data are on CloudResearch's Sequel servers located at Queens College in the city of New York. CloudResearch makes data available to be accessed by researchers for replication purposes, on the CloudResearch premises, in the same way the data were accessed and analysed by the authors of this manuscript. The contact person at CloudResearch who can help researchers access the data set is Tzvi Abberbock, who can be reached at [email protected] .

Studies of the gender pay gap are seldom able to simultaneously account for the range of alternative putative mechanisms underlying it. Using CloudResearch, an online microtask platform connecting employers to workers who perform research-related tasks, we examine whether gender pay discrepancies are still evident in a labor market characterized by anonymity, relatively homogeneous work, and flexibility. For 22,271 Mechanical Turk workers who participated in nearly 5 million tasks, we analyze hourly earnings by gender, controlling for key covariates which have been shown previously to lead to differential pay for men and women. On average, women’s hourly earnings were 10.5% lower than men’s. Several factors contributed to the gender pay gap, including the tendency for women to select tasks that have a lower advertised hourly pay. This study provides evidence that gender pay gaps can arise despite the absence of overt discrimination, labor segregation, and inflexible work arrangements, even after experience, education, and other human capital factors are controlled for. Findings highlight the need to examine other possible causes of the gender pay gap. Potential strategies for reducing the pay gap on online labor markets are also discussed.

Introduction

The gender pay gap, the disparity in earnings between male and female workers, has been the focus of empirical research in the US for decades, as well as legislative and executive action under the Obama administration [ 1 , 2 ]. Trends dating back to the 1960s show a long period in which women’s earnings were approximately 60% of their male counterparts, followed by increases in women’s earnings starting in the 1980s, which began to narrow, but not close, the gap which persists today [ 3 ]. More recent data from 2014 show that overall, the median weekly earnings of women working full time were 79–83% of what men earned [ 4 – 9 ].

The extensive literature seeking to explain the gender pay gap and its trajectory over time in traditional labor markets suggests it is a function of multiple structural and individual-level processes that reflect both the near-term and cumulative effects of gender relations and roles over the life course. Broadly speaking, the drivers of the gender pay gap can be categorized as: 1) human capital or productivity factors such as education, skills, and workforce experience; 2) industry or occupational segregation, which some estimates suggest accounts for approximately half of the pay gap; 3) gender-specific temporal flexibility constraints which can affect promotions and remuneration; and finally, 4) gender discrimination operating in hiring, promotion, task assignment, and/or compensation. The latter mechanism is often estimated by inference as a function of unexplained residual effects of gender on payment after accounting for other factors, an approach which is most persuasive in studies of narrowly restricted populations of workers such as lawyers [ 10 ] and academics of specific disciplines [ 11 ]. A recent estimate suggests this unexplained gender difference in earnings can account for approximately 40% of the pay gap [ 3 ]. However, more direct estimations of discriminatory processes are also available from experimental evidence, including field audit and lab-based studies [ 12 – 14 ]. Finally, gender pay gaps have also been attributed to differential discrimination encountered by men and women on the basis of parental status, often known as the ‘motherhood penalty’ [ 15 ].

Non-traditional ‘gig economy’ labor markets and the gender pay gap

In recent years there has been a dramatic rise in nontraditional ‘gig economy’ labor markets, which entail independent workers hired for single projects or tasks often on a short-term basis with minimal contractual engagement. “Microtask” platforms such as Amazon Mechanical Turk (MTurk) and Crowdflower have become a major sector of the gig economy, offering a source of easily accessible supplementary income through performance of small tasks online at a time and place convenient to the worker. Available tasks can range from categorizing receipts to transcription and proofreading services, and are posted online by the prospective employer. Workers registered with the platform then elect to perform the advertised tasks and receive compensation upon completion of satisfactory work [ 16 ]. An estimated 0.4% of US adults are currently receiving income from such platforms each month [ 17 ], and microtask work is a growing sector of the service economy in the United States [ 18 ]. Although still relatively small, these emerging labor market environments provide a unique opportunity to investigate the gender pay gap in ways not possible within traditional labor markets, due to features (described below) that allow researchers to simultaneously account for multiple putative mechanisms thought to underlie the pay gap.

The present study utilizes the Amazon Mechanical Turk (MTurk) platform as a case study to examine whether a gender pay gap remains evident when the main causes of the pay gap identified in the literature do not apply or can be accounted for in a single investigation. MTurk is an online microtask platform that connects employers (‘requesters’) to employees (‘workers’) who perform jobs called “Human Intelligence Tasks” (HITs). The platform allows requesters to post tasks on a dashboard with a short description of the HIT, the compensation being offered, and the time the HIT is expected to take. When complete, the requester either approves or rejects the work based on quality. If approved, payment is quickly accessible to workers. The gender of workers who complete these HITs is not known to the requesters, but was accessible to researchers for the present study (along with other sociodemographic information and pay rates) based on metadata collected through CloudResearch (formerly TurkPrime), a platform commonly used to conduct social and behavioral research on MTurk [ 19 ].

Evaluating pay rates of workers on MTurk requires estimating the pay per hour of each task that a worker accepts which can then be averaged together. All HITs posted on MTurk through CloudResearch display how much a HIT pays and an estimated time that it takes for that HIT to be completed. Workers use this information to determine what the corresponding hourly pay rate of a task is likely to be, and much of our analysis of the gender pay gap is based on this advertised pay rate of all completed surveys. We also calculate an estimate of the gender pay gap based on actual completion times to examine potential differences in task completion speed, which we refer to as estimated actual wages (see Methods section for details).

Previous studies have found that both task completion time and the selection of tasks influences the gender pay gap in at least some gig economy markets. For example, a gender pay gap was observed among Uber drivers, with men consistently earning higher pay than women [ 20 ]. Some of the contributing factors to this pay gap include that male Uber drivers selected different tasks than female drivers, including being more willing to work at night and to work in neighborhoods that were perceived to be more dangerous. Male drivers were also likely to drive faster than their female counterparts. These findings show that person-level factors like task selection, and speed can influence the gender pay gap within gig economy markets.

MTurk is uniquely suited to examine the gender pay gap because it is possible to account simultaneously for multiple structural and individual-level factors that have been shown to produce pay gaps. These include discrimination, work heterogeneity (leading to occupational segregation), and job flexibility, as well as human capital factors such as experience and education.

Discrimination

When employers post their HITs on MTurk they have no way of knowing the demographic characteristics of the workers who accept those tasks, including their gender. While MTurk allows for selective recruitment of specific demographic groups, the MTurk tasks examined in this study are exclusively open to all workers, independent of their gender or other demographic characteristics. Therefore, features of the worker’s identity that might be the basis for discrimination cannot factor into an employer’s decision-making regarding hiring or pay.

Task heterogeneity

Another factor making MTurk uniquely suited for the examination of the gender pay gap is the relative homogeneity of tasks performed by the workers, minimizing the potential influence of gender differences in the type of work pursued on earnings and the pay gap. Work on the MTurk platform consists mostly of short tasks such as 10–15 minute surveys and categorization tasks. In addition, the only information that workers have available to them to choose tasks, other than pay, is the tasks’ titles and descriptions. We additionally classified tasks based on similarity and accounted for possible task heterogeneity effects in our analyses.

Job flexibility

MTurk is not characterized by the same inflexibilities as are often encountered in traditional labor markets. Workers can work at any time of the day or day of the week. This increased flexibility may be expected to provide more opportunities for participation in this labor market for those who are otherwise constrained by family or other obligations.

Human capital factors

It is possible that the more experienced workers could learn over time how to identify higher paying tasks by virtue of, for example, identifying qualities of tasks that can be completed more quickly than the advertised required time estimate. Further, if experience is correlated with gender, it could contribute to a gender pay gap and thus needs to be controlled for. Using CloudResearch metadata, we are able to account for experience on the platform. Additionally, we account for multiple sociodemographic variables, including age, marital status, parental status, education, income (from all sources), and race using the sociodemographic data available through CloudResearch.

Expected gender pay gap findings on MTurk

Due to the aforementioned factors that are unique to the MTurk marketplace–e.g., anonymity, self-selection into tasks, relative homogeneity of the tasks performed, and flexible work scheduling–we did not expect a gender pay gap to be evident on the platform to the same extent as in traditional labor markets. However, potential gender differences in task selection and completion speed, which have implications for earnings, merit further consideration. For example, though we expect the relative homogeneity of the MTurk tasks to minimize gender differences in task selection that could mimic occupational segregation, we do account for potential subtle residual differences in tasks that could differentially attract male and female workers and indirectly lead to pay differentials if those tasks that are preferentially selected by men pay a higher rate. To do this we categorize all tasks based on their descriptions using K-clustering and add the clusters as covariates to our models. In addition, we separately examine the gender pay gap within each topic-cluster.

In addition, if workers who are experienced on the platform are better able to find higher paying HITs, and if experience is correlated with gender, it may lead to gender differences in earnings. Theoretically, other factors that may vary with gender could also influence task selection. Previous studies of the pay gap in traditional markets indicate that reservation wages, defined as the pay threshold at which a person is willing to accept work, may be lower among women with children compared to women without, and to that of men as well [ 21 ]. Thus, if women on MTurk are more likely to have young children than men, they may be more willing to accept available work even if it pays relatively poorly. Other factors such as income, education level, and age may similarly influence reservation wages if they are associated with opportunities to find work outside of microtask platforms. To the extent that these demographics correlate with gender they may give rise to a gender pay gap. Therefore we consider age, experience on MTurk, education, income, marital status, and parental status as covariates in our models.

Task completion speed may vary by gender for several reasons, including potential gender differences in past experience on the platform. We examine the estimated actual pay gap per hour based on HIT payment and estimated actual completion time to examine the effects of completion speed on the wage gap. We also examine the gender pay gap based on advertised pay rates, which are not dependent on completion speed and more directly measure how gender differences in task selection can lead to a pay gap. Below, we explain how these were calculated based on meta-data from CloudResearch.

To summarize, the overall goal of the present study was to explore whether gender pay differentials arise within a unique, non-traditional and anonymous online labor market, where known drivers of the gender pay gap either do not apply or can be accounted for statistically.

Materials and methods

Amazon mechanical turk and cloudresearch.

Started in 2005, the original purpose of the Amazon Mechanical Turk (MTurk) platform was to allow requesters to crowdsource tasks that could not easily be handled by existing technological solutions such as receipt copying, image categorization, and website testing. As of 2010, researchers increasingly began using MTurk for a wide variety of research tasks in the social, behavioral, and medical sciences, and it is currently used by thousands of academic researchers across hundreds of academic departments [ 22 ]. These research-related HITs are typically listed on the platform in generic terms such as, “Ten-minute social science study,” or “A study about public opinion attitudes.”

Because MTurk was not originally designed solely for research purposes, its interface is not optimized for some scientific applications. For this reason, third party add-on toolkits have been created that offer critical research tools for scientific use. One such platform, CloudResearch (formerly TurkPrime), allows requesters to manage multiple research functions, such as applying sampling criteria and facilitating longitudinal studies, through a link to their MTurk account. CloudResearch’s functionality has been described extensively elsewhere [ 19 ]. While the demographic characteristics of workers are not available to MTurk requesters, we were able to retroactively identify the gender and other demographic characteristics of workers through the CloudResearch platform. CloudResearch also facilitates access to data for each HIT, including pay, estimated length, and title.

The study was an analysis of previously collected metadata, which were analyzed anonymously. We complied with the terms of service for all data collected from CloudResearch, and MTurk. The approving institutional review board for this study was IntegReview.

Analytic sample

We analyzed the nearly 5 million tasks completed during an 18-month period between January 2016 and June 2017 by 12,312 female and 9,959 male workers who had complete data on key demographic characteristics. To be included in the analysis a HIT had to be fully completed, not just accepted, by the worker, and had to be accepted (paid for) by the requester. Although the vast majority of HITs were open to both males and females, a small percentage of HITs are intended for a specific gender. Because our goal was to exclusively analyze HITs for which the requesters did not know the gender of workers, we excluded any HITs using gender-specific inclusion or exclusion criteria from the analyses. In addition, we removed from the analysis any HITs that were part of follow-up studies in which it would be possible for the requester to know the gender of the worker from the prior data collection. Finally, where possible, CloudResearch tracks demographic information on workers across multiple HITs over time. To minimize misclassification of gender, we excluded the 0.3% of assignments for which gender was unknown with at least 95% consistency across HITs.

The main exposure variable is worker gender and the outcome variables are estimated actual hourly pay accrued through completing HITs, and advertised hourly pay for completed HITs. Estimated actual hourly wages are based on the estimated length in minutes and compensation in dollars per HIT as posted on the dashboard by the requester. We refer to actual pay as estimated because sometimes people work multiple assignments at the same time (which is allowed on the platform), or may simultaneously perform other unrelated activities and therefore not work on the HIT the entire time the task is open. We also considered several covariates to approximate human capital factors that could potentially influence earnings on this platform, including marital status, education, household income, number of children, race/ethnicity, age, and experience (number of HITs previously completed). Additional covariates included task length, task cluster (see below), and the serial order with which workers accepted the HIT in order to account for potential differences in HIT acceptance speed that may relate to the pay gap.

Database and analytic approach

Data were exported from CloudResearch’s database into Stata in long-form format to represent each task on a single row. For the purposes of this paper, we use “HIT” and “study” interchangeably to refer to a study put up on the MTurk dashboard which aims to collect data from multiple participants. A HIT or study consist of multiple “assignments” which is a single task completed by a single participant. Columns represented variables such as demographic information, payment, and estimated HIT length. Column variables also included unique IDs for workers, HITs (a single study posted by a requester), and requesters, allowing for a multi-level modeling analytic approach with assignments nested within workers. Individual assignments (a single task completed by a single worker) were the unit of analysis for all models.

Linear regression models were used to calculate the gender pay gap using two dependent variables 1) women’s estimated actual earnings relative to men’s and 2) women’s selection of tasks based on advertised earnings relative to men’s. We first examined the actual pay model, to see the gender pay gap when including an estimate of task completion speed, and then adjusted this model for advertised hourly pay to determine if and to what extent a propensity for men to select more remunerative tasks was evident and driving any observed gender pay gap. We additionally ran separate models using women’s advertised earnings relative to men’s as the dependent variable to examine task selection effects more directly. The fully adjusted models controlled for the human capital-related covariates, excluding household income and education which were balanced across genders. These models also tested for interactions between gender and each of the covariates by adding individual interaction terms to the adjusted model. To control for within-worker clustering, Huber-White standard error corrections were used in all models.

Cluster analysis

To explore the potential influence of any residual task heterogeneity and gender preference for specific task type as the cause of the gender pay gap, we use K-means clustering analysis (seed = 0) to categorize the types of tasks into clusters based on the descriptions that workers use to choose the tasks they perform. We excluded from this clustering any tasks which contained certain gendered words (such as “male”, “female”, etc.) and any tasks which had fewer than 30 respondents. We stripped out all punctuation, symbols and digits from the titles, so as to remove any reference to estimated compensation or duration. The features we clustered on were the presence or absence of 5,140 distinct words that appeared across all titles. We then present the distribution of tasks across these clusters as well as average pay by gender and the gender pay gap within each cluster.

The demographics of the analytic sample are presented in Table 1 . Men and women completed comparable numbers of tasks during the study period; 2,396,978 (48.6%) for men and 2,539,229 (51.4%) for women.

In Table 2 we measure the differences in remuneration between genders, and then decompose any observed pay gap into task completion speed, task selection, and then demographic and structural factors. Model 1 shows the unadjusted regression model of gender differences in estimated actual pay, and indicates that, on average, tasks completed by women paid 60 (10.5%) cents less per hour compared to tasks completed by men (t = 17.4, p < .0001), with the mean estimated actual pay across genders being $5.70 per hour.

*Model adjusted for race, marital status, number of children and task clusters as categorical covariates, and age, HIT acceptance speed, and number of HITs as continuous covariates.

In Model 2, adjusting for advertised hourly pay, the gender pay gap dropped to 46 cents indicating that 14 cents of the pay gap is attributable to gender differences in the selection of tasks (t = 8.6, p < .0001). Finally, after the inclusion of covariates and their interactions in Model 3, the gender pay differential was further attenuated to 32 cents (t = 6.7, p < .0001). The remaining 32 cent difference (56.6%) in earnings is inferred to be attributable to gender differences in HIT completion speed.

Task selection analyses

Although completion speed appears to account for a significant portion of the pay gap, of particular interest are gender differences in task selection. Beyond structural factors such as education, household composition and completion speed, task selection accounts for a meaningful portion of the gender pay gap. As a reminder, the pay rate and expected completion time are posted for every HIT, so why women would select less remunerative tasks on average than men do is an important question to explore. In the next section of the paper we perform a set of analyses to examine factors that could account for this observed gender difference in task selection.

Advertised hourly pay

To examine gender differences in task selection, we used linear regression to directly examine whether the advertised hourly pay differed for tasks accepted by male and female workers. We first ran a simple model ( Table 3 ; Model 3A) on the full dataset of 4.93 million HITs, with gender as the predictor and advertised hourly pay as the outcome including no other covariates. The unadjusted regression results (Model 4) shown in Table 3 , indicates that, summed across all clusters and demographic groups, tasks completed by women were advertised as paying 28 cents (95% CI: $0.25-$0.31) less per hour (5.8%) compared to tasks completed by men (t = 21.8, p < .0001).

*Models adjusted for race, marital status, number of children, and task clusters as categorical covariates, and age, HIT acceptance speed, and number of HITs as continuous covariates.

Model 5 examines whether the remuneration differences for tasks selected by men and women remains significant in the presence of multiple covariates included in the previous model and their interactions. The advertised pay differential for tasks selected by women compared to men was attenuated to 21 cents (4.3%), and remained statistically significant (t = 9.9, p < .0001). This estimate closely corresponded to the inferred influence of task selection reported in Table 2 . Tests of gender by covariate interactions were significant only in the cases of age and marital status; the pay differential in tasks selected by men and women decreased with age and was more pronounced among single versus currently or previously married women.

To further examine what factors may account for the observed gender differences in task selection we plotted the observed pay gap within demographic and other covariate groups. Table 4 shows the distribution of tasks completed by men and women, as well as mean earnings and the pay gap across all demographic groups, based on the advertised (not actual) hourly pay for HITs selected (hereafter referred to as “advertised hourly pay” and the “advertised pay gap”). The average task was advertised to pay $4.88 per hour (95% CI $4.69, $5.10).

The pattern across demographic characteristics shows that the advertised hourly pay gap between genders is pervasive. Notably, a significant advertised gender pay gap is evident in every level of each covariate considered in Table 4 , but more pronounced among some subgroups of workers. For example, the advertised pay gap was highest among the youngest workers ($0.31 per hour for workers age 18–29), and decreased linearly with age, declining to $0.13 per hour among workers age 60+. Advertised houry gender pay gaps were evident across all levels of education and income considered.

To further examine the potential influence of human capital factors on the advertised hourly pay gap, Table 5 presents the average advertised pay for selected tasks by level of experience on the CloudResearch platform. Workers were grouped into 4 experience levels, based on the number of prior HITs completed: Those who completed fewer than 100 HITs, between 100 and 500 HITs, between 500 and 1,000 HITs, and more than 1,000 HITs. A significant gender difference in advertised hourly pay was observed within each of these four experience groups. The advertised hourly pay for tasks selected by both male and female workers increased with experience, while the gender pay gap decreases. There was some evidence that male workers have more cumulative experience with the platform: 43% of male workers had the highest level of experience (previously completing 1,001–10,000 HITs) compared to only 33% of women.

Table 5 also explores the influence of task heterogeneity upon HIT selection and the gender gap in advertised hourly pay. K-means clustering was used to group HITs into 20 clusters initially based on the presence or absence of 5,140 distinct words appearing in HIT titles. Clusters with fewer than 50,000 completed tasks were then excluded from analysis. This resulted in 13 clusters which accounted for 94.3% of submitted work assignments (HITs).

The themes of all clusters as well as the average hourly advertised pay for men and women within each cluster are presented in the second panel of Table 5 . The clusters included categories such as Games, Decision making, Product evaluation, Psychology studies, and Short Surveys. We did not observe a gender preference for any of the clusters. Specifically, for every cluster, the proportion of males was no smaller than 46.6% (consistent with the slightly lower proportion of males on the platform, see Table 1 ) and no larger than 50.2%. As shown in Table 5 , the gender pay gap was observed within each of the clusters. These results suggest that residual task heterogeneity, a proxy for occupational segregation, is not likely to contribute to a gender pay gap in this market.

Task length was defined as the advertised estimated duration of a HIT. Table 6 presents the advertised hourly gender pay gaps for five categories of HIT length, which ranged from a few minutes to over 1 hour. Again, a significant advertised hourly gender pay gap was observed in each category.

Finally, we conducted additional supplementary analyses to determine if other plausible factors such as HIT timing could account for the gender pay gap. We explored temporal factors including hour of the day and day of the week. Each completed task was grouped based on the hour and day in which it was completed. A significant advertised gender pay gap was observed within each of the 24 hours of the day and for every day of the week demonstrating that HIT timing could not account for the observed gender gap (results available in Supplementary Materials).

In this study we examined the gender pay gap on an anonymous online platform across an 18-month period, during which close to five million tasks were completed by over 20,000 unique workers. Due to factors that are unique to the Mechanical Turk online marketplace–such as anonymity, self-selection into tasks, relative homogeneity of the tasks performed, and flexible work scheduling–we did not expect earnings to differ by gender on this platform. However, contrary to our expectations, a robust and persistent gender pay gap was observed.

The average estimated actual pay on MTurk over the course of the examined time period was $5.70 per hour, with the gender pay differential being 10.5%. Importantly, gig economy platforms differ from more traditional labor markets in that hourly pay largely depends on the speed with which tasks are completed. For this reason, an analysis of gender differences in actual earned pay will be affected by gender differences in task completion speed. Unfortunately, we were not able to directly measure the speed with which workers complete tasks and account for this factor in our analysis. This is because workers have the ability to accept multiple HITs at the same time and multiple HITs can sit dormant in a queue, waiting for workers to begin to work on them. Therefore, the actual time that many workers spend working on tasks is likely less than what is indicated in the metadata available. For this reason, the estimated average actual hourly rate of $5.70 is likely an underestimate and the gender gap in actual pay cannot be precisely measured. We infer however, by the residual gender pay gap after accounting for other factors, that as much as 57% (or $.32) of the pay differential may be attributable to task completion speed. There are multiple plausible explanations for gender differences in task completion speed. For example, women may be more meticulous at performing tasks and, thus, may take longer at completing them. There may also be a skill factor related to men’s greater experience on the platform (see Table 5 ), such that men may be faster on average at completing tasks than women.

However, our findings also revealed another component of a gender pay gap on this platform–gender differences in the selection of tasks based on their advertised pay. Because the speed with which workers complete tasks does not impact these estimates, we conducted extensive analyses to try to explain this gender gap and the reasons why women appear on average to be selecting tasks that pay less compared to men. These results pertaining to the advertised gender pay gap constitute the main focus of this study and the discussion that follows.

The overall advertised hourly pay was $4.88. The gender pay gap in the advertised hourly pay was $0.28, or 5.8% of the advertised pay. Once a gender earnings differential was observed based on advertised pay, we expected to fully explain it by controlling for key structural and individual-level covariates. The covariates that we examined included experience, age, income, education, family composition, race, number of children, task length, the speed of accepting a task, and thirteen types of subtasks. We additionally examined the time of day and day of the week as potential explanatory factors. Again, contrary to our expectations, we observed that the pay gap persisted even after these potential confounders were controlled for. Indeed, separate analyses that examined the advertised pay gap within each subcategory of the covariates showed that the pay gap is ubiquitous, and persisted within each of the ninety sub-groups examined. These findings allows us to rule out multiple mechanisms that are known drivers of the pay gap in traditional labor markets and other gig economy marketplaces. To our knowledge this is the only study that has observed a pay gap across such diverse categories of workers and conditions, in an anonymous marketplace, while simultaneously controlling for virtually all variables that are traditionally implicated as causes of the gender pay gap.

Individual-level factors

Individual-level factors such as parental status and family composition are a common source of the gender pay gap in traditional labor markets [ 15 ] . Single mothers have previously been shown to have lower reservation wages compared to other men and women [ 21 ]. In traditional labor markets lower reservation wages lead single mothers to be willing to accept lower-paying work, contributing to a larger gender pay gap in this group. This pattern may extend to gig economy markets, in which single mothers may look to online labor markets as a source of supplementary income to help take care of their children, potentially leading them to become less discriminating in their choice of tasks and more willing to work for lower pay. Since female MTurk workers are 20% more likely than men to have children (see Table 1 ), it was critical to examine whether the gender pay gap may be driven by factors associated with family composition.

An examination of the advertised gender pay gap among individuals who differed in their marital and parental status showed that while married workers and those with children are indeed willing to work for lower pay (suggesting that family circumstances do affect reservation wages and may thus affect the willingness of online workers to accept lower-paying online tasks), women’s hourly pay is consistently lower than men’s within both single and married subgroups of workers, and among workers who do and do not have children. Indeed, contrary to expectations, the advertised gender pay gap was highest among those workers who are single, and among those who do not have any children. This observation shows that it is not possible for parental and family status to account for the observed pay gap in the present study, since it is precisely among unmarried individuals and those without children that the largest pay gap is observed.

Age was another factor that we considered to potentially explain the gender pay gap. In the present sample, the hourly pay of older individuals is substantially lower than that of younger workers; and women on the platform are five years older on average compared to men (see Table 1 ). However, having examined the gender pay gap separately within five different age cohorts we found that the largest pay gap occurs in the two youngest cohort groups: those between 18 and 29, and between 30 and 39 years of age. These are also the largest cohorts, responsible for 64% of completed work in total.

Younger workers are also most likely to have never been married or to not have any children. Thus, taken together, the results of the subgroup analyses are consistent in showing that the largest pay gap does not emerge from factors relating to parental, family, or age-related person-level factors. Similar patterns were found for race, education, and income. Specifically, a significant gender pay gap was observed within each subgroup of every one of these variables, showing that person-level factors relating to demographics are not driving the pay gap on this platform.

Experience is a factor that has an influence on the pay gap in both traditional and gig economy labor markets [ 20 ] . As noted above, experienced workers may be faster and more efficient at completing tasks in this platform, but also potentially more savvy at selecting more remunerative tasks compared to less experienced workers if, for example, they are better at selecting tasks that will take less time to complete than estimated on the dashboard [ 20 ]. On MTurk, men are overall more experienced than women. However, experience does not account for the gender gap in advertised pay in the present study. Inexperienced workers comprise the vast majority of the Mechanical Turk workforce, accounting for 67% of all completed tasks (see Table 5 ). Yet within this inexperienced group, there is a consistent male earning advantage based on the advertised pay for tasks performed. Further, controlling for the effect of experience in our models has a minimal effect on attenuating the gender pay gap.

Another important source of the gender pay gap in both traditional and gig economy labor markets is task heterogeneity. In traditional labor markets men are disproportionately represented in lucrative fields, such as those in the tech sector [ 23 ]. While the workspace within MTurk is relatively homogeneous compared to the traditional labor market, there is still some variety in the kinds of tasks that are available, and men and women may have been expected to have preferences that influence choices among these.

To examine whether there is a gender preference for specific tasks, we systematically analyzed the textual descriptions of all tasks included in this study. These textual descriptions were available for all workers to examine on their dashboards, along with information about pay. The clustering algorithm revealed thirteen categories of tasks such as games, decision making, several different kinds of survey tasks, and psychology studies.We did not observe any evidence of gender preference for any of the task types. Within each of the thirteen clusters the distribution of tasks was approximately equally split between men and women. Thus, there is no evidence that women as a group have an overall preference for specific tasks compared to men. Critically, the gender pay gap was also observed within each one of these thirteen clusters.

Another potential source of heterogeneity is task length. Based on traditional labor markets, one plausible hypothesis about what may drive women’s preferences for specific tasks is that women may select tasks that differ in their duration. For example, women may be more likely to use the platform for supplemental income, while men may be more likely to work on HITs as their primary income source. Women may thus select shorter tasks relative to their male counterparts. If the shorter tasks pay less money, this would result in what appears to be a gender pay gap.

However, we did not observe gender differences in task selection based on task duration. For example, having divided tasks into their advertised length, the tasks are preferred equally by men and women. Furthermore, the shorter tasks’ hourly pay is substantially higher on average compared to longer tasks.

Additional evidence that scheduling factors do not drive the gender pay gap is that it was observed within all hourly and daily intervals (See S1 and S2 Tables in Appendix). These data are consistent with the results presented above regarding personal level factors, showing that the majority of male and female Mechanical Turk workers are single, young, and have no children. Thus, while in traditional labor markets task heterogeneity and labor segmentation is often driven by family and other life circumstances, the cohort examined in this study does not appear to be affected by these factors.

Practical implications of a gender pay gap on online platforms for social and behavioral science research

The present findings have important implications for online participant recruitment in the social and behavioral sciences, and also have theoretical implications for understanding the mechanisms that give rise to the gender pay gap. The last ten years have seen a revolution in data collection practices in the social and behavioral sciences, as laboratory-based data collection has slowly and steadily been moving online [ 16 , 24 ]. Mechanical Turk is by far the most widely used source of human participants online, with thousands of published peer-reviewed papers utilizing Mechanical Turk to recruit at least some of their human participants [ 25 ]. The present findings suggest both a challenge and an opportunity for researchers utilizing online platforms for participant recruitment. Our findings clearly reveal for the first time that sampling research participants on anonymous online platforms tends to produce gender pay inequities, and that this happens independent of demographics or type of task. While it is not clear from our findings what the exact cause of this inequity is, what is clear is that the online sampling environment produces similar gender pay inequities as those observed in other more traditional labor markets, after controlling for relevant covariates.

This finding is inherently surprising since many mechanisms that are known to produce the gender pay gap in traditional labor markets are not at play in online microtasks environments. Regardless of what the generative mechanisms of the gender pay gap on online microtask platforms might be, researchers may wish to consider whether changes in their sampling practices may produce more equitable pay outcomes. Unlike traditional labor markets, online data collection platforms have built-in tools that can allow researchers to easily fix gender pay inequities. Researchers can simply utilize gender quotas, for example, to fix the ratio of male and female participants that they recruit. These simple fixes in sampling practices will not only produce more equitable pay outcomes but are also most likely advantageous for reducing sampling bias due to gender being correlated with pay. Thus, while our results point to a ubiquitous discrepancy in pay between men and women on online microtask platforms, such inequities have relatively easy fixes on online gig economy marketplaces such as MTurk, compared to traditional labor markets where gender-based pay inequities have often remained intractable.

Other gig economy markets

As discussed in the introduction, a gender wage gap has been demonstrated on Uber, a gig economy transportation marketplace [ 20 ], where men earn approximately 7% more than women. However, unlike in the present study, the gender wage gap on Uber was fully explained by three factors; a) driving speed predicted higher wages, with men driving faster than women, b) men were more likely than women to drive in congested locations which resulted in better pay, c) experience working for Uber predicted higher wages, with men being more experienced. Thus, contrary to our findings, the gender wage gap in gig economy markets studied thus far are fully explained by task heterogeneity, experience, and task completion speed. To our knowledge, the results presented in the present study are the first to show that the gender wage gap can emerge independent of these factors.

Generalizability

Every labor market is characterized by a unique population of workers that are almost by definition not a representation of the general population outside of that labor market. Likewise, Mechanical Turk is characterized by a unique population of workers that is known to differ from the general population in several ways. Mechanical Turk workers are younger, better educated, less likely to be married or have children, less likely to be religious, and more likely to have a lower income compared to the general United States population [ 24 ]. The goal of the present study was not to uncover universal mechanisms that generate the gender pay gap across all labor markets and demographic groups. Rather, the goal was to examine a highly unique labor environment, characterized by factors that should make this labor market immune to the emergence of a gender pay gap.

Previous theories accounting for the pay gap have identified specific generating mechanisms relating to structural and personal factors, in addition to discrimination, as playing a role in the emergence of the gender pay gap. This study examined the work of over 20,000 individuals completing over 5 million tasks, under conditions where standard mechanisms that generate the gender pay gap have been controlled for. Nevertheless, a gender pay gap emerged in this environment, which cannot be accounted for by structural factors, demographic background, task preferences, or discrimination. Thus, these results reveal that the gender pay gap can emerge—in at least some labor markets—in which discrimination is absent and other key factors are accounted for. These results show that factors which have been identified to date as giving rise to the gender pay gap are not sufficient to explain the pay gap in at least some labor markets.

Potential mechanisms

While we cannot know from the results of this study what the actual mechanism is that generates the gender pay gap on online platforms, we suggest that it may be coming from outside of the platform. The particular characteristics of this labor market—such as anonymity, relative task homogeneity, and flexibility—suggest that, everything else being equal, women working in this platform have a greater propensity to choose less remunerative opportunities relative to men. It may be that these choices are driven by women having a lower reservation wage compared to men [ 21 , 26 ]. Previous research among student populations and in traditional labor markets has shown that women report lower pay or reward expectations than men [ 27 – 29 ]. Lower pay expectations among women are attributed to justifiable anticipation of differential returns to labor due to factors such as gender discrimination and/or a systematic psychological bias toward pessimism relative to an overly optimistic propensity among men [ 30 ].

Our results show that even if the bias of employers is removed by hiding the gender of workers as happens on MTurk, it seems that women may select lower paying opportunities themselves because their lower reservation wage influences the types of tasks they are willing to work on. It may be that women do this because cumulative experiences of pervasive discrimination lead women to undervalue their labor. In turn, women’s experiences with earning lower pay compared to men on traditional labor markets may lower women’s pay expectations on gig economy markets. Thus, consistent with these lowered expectations, women lower their reservation wages and may thus be more likely than men to settle for lower paying tasks.

More broadly, gender norms, psychological attributes, and non-cognitive skills, have recently become the subject of investigation as a potential source for the gender pay gap [ 3 ], and the present findings indicate the importance of such mechanisms being further explored, particularly in the context of task selection. More research will be required to explore the potential psychological and antecedent structural mechanisms underlying differential task selection and expectations of compensation for time spent on microtask platforms, with potential relevance to the gender pay gap in traditional labor markets as well. What these results do show is that pay discrepancies can emerge despite the absence of discrimination in at least some circumstances. These results should be of particular interest for researchers who may wish to see a more equitable online labor market for academic research, and also suggest that novel and heretofore unexplored mechanisms may be at play in generating these pay discrepancies.

A final note about framing: we are aware that explanations of the gender pay gap that invoke elements of women’s agency and, more specifically, “choices” risk both; a) diminishing or distracting from important structural factors, and b) “naturalizing” the status quo of gender inequality [ 30 ] . As Connor and Fiske (2019) argue, causal attributions for the gender pay gap to “unconstrained choices” by women, common as part of human capital explanations, may have the effect, intended or otherwise, of reinforcing system-justifying ideologies that serve to perpetuate inequality. By explicitly locating women’s economic decision making on the MTurk platform in the broader context of inegalitarian gender norms and labor market experiences outside of it (as above), we seek to distance our interpretation of our findings from implicit endorsement of traditional gender roles and economic arrangements and to promote further investigation of how the observed gender pay gap in this niche of the gig economy may reflect both broader gender inequalities and opportunities for structural remedies.

Supporting information

Funding statement.

The authors received no specific funding for this work.

Data Availability

How to Counter Nine Arguments About The Gender Pay Gap

argumentative essay on gender pay gap

Tuesday, April 4 is Equal Pay Day, a day which symbolizes how far into the current year women must work in order to make the same amount of income that men made last year. As special attention is given to the gender wage gap today, you may hear many frustrating arguments about the pay gap that exists between men and women. In order to help navigate these arguments, the following is a guide debunking some of the most common erroneous assumptions about Equal Pay Day and the gender wage gap.

This year's Equal Pay Day demonstrates that, on average, in order to make the same salary as men made the previous year, women must work 44 days into the following year. This reflects an overall wage disparity of American women making, on average, 79 cents for every dollar that the average American man makes . Equal Pay Day is designed to draw attention to this disparity as well as to inspire others to take concrete action to remedy the issue.

However, while this day is meant as one of collective action and advocacy, it is also often punctuated with arguments from skeptics about why unequal pay is not discriminatory or why it exists in the first place. These arguments are certainly frustrating to encounter, particularly on a day meant to highlight inequitable treatment, and thus it is helpful to have counterpoints ready in case you do encounter them, today and everyday.

1. Women Don't Negotiate Their Pay

Some argue that the gender wage gap exists simply because women supposedly do not negotiate their salaries as well or as frequently as men. According to The Guardian, this notion is patently inaccurate. Women recognize the importance of negotiation and do indeed negotiate for higher salaries — they are just less likely to receive them , which is evidence of pay discrimination.

Furthermore, women actually run a much higher risk than men of being socially or professionally penalized for negotiating for more income. According to MONEY reporting on a Harvard Business Review article, "In repeated studies, the social cost of negotiating for higher pay has been found to be greater for women than it is for men... Ask your boss for more money and risk being seen as ungrateful and pushy; don't ask for more and you'll be paid less for the rest of your career."

Thus, discrimination in negotiation practices constitutes evidence of the equal pay gap and why it exists — and not an argument against it.

2. Women "Choose" Lower-Paying Jobs

argumentative essay on gender pay gap

Those who question the discriminatory component of the gender wage gap often say that women simply choose lower-paying jobs, which is why the gap exists. In fact, quite the opposite of this notion is true. Evidence demonstrates that when women choose jobs, the pay tends to decrease . This is demonstrated both in the average salaries for female-dominated professions as well as in overall sector salary decreases when more women choose to enter a profession that was previously dominated by men.

A sociology study cited in The Guardian was enlightening in regard to this issue and noted, "...When women enter an occupation in large numbers, that job begins to pay less , even after controlling for a range of factors like skill, race and geography... [Furthermore], their analysis found...that a higher proportion of women in an occupation leads to lower pay because of the discounting of work performed by women."

Thus, sadly, jobs that women choose often end up paying them less simply because of their gender, not because they chose a profession that was inherently low-paying.

3. Women Leave The Workforce To Have Children

argumentative essay on gender pay gap

Another oft-cited argument regarding the gender pay gap is that women voluntarily leave the workforce to have children and then are simply paid less because they have been out of the workforce for years and are resultantly less experienced.

However, this argument is again inaccurate for a variety of reasons. According to a study done by The Guardian, sex discrimination still constitutes the biggest reason behind the gender pay gap, even after taking into account factors like "age, tenure, time out of the workforce, occupation, industry, part-time work and sector." Furthermore, women are seemingly penalized for having children once they do return to work.

According to the Telegraph, women can earn up to a third less than men when they return to work after childbirth, while men actually earn up to 6 percent more after having a child, according to Business Insider.

Thus, a woman temporarily exiting the workforce to care for her children does not account for the gender pay gap.

4. Men Work Harder Than Women

While this seems like a ridiculous argument to have to debunk in 2017, unfortunately arguments regarding the wage gap sometimes do reflect the (erroneous) belief that men work harder than women and therefore should be paid more. According to MONEY , critics often say that men "put in more hours, don't take as much time off, and don't leave temporarily to have children." However, these statements are sweeping generalizations and are also inaccurate.

According to the New York Times, it is actually women who do more at work, but simply benefit less from it in terms of recognition. The article cited several studies that found that women are more likely to stay late at work as well as to engage in "office housework," or administrative tasks that are helpful but do not "pay off," professionally-speaking. According to the article, " women help more [at work] but benefit less from it." Furthermore, when women return home in the evenings, they are also still largely responsible for household tasks , limiting their options to continue their professional work into evening hours.

Thus, the notion that women not working as hard as men contributes to the gender pay gap constitutes a highly inaccurate argument that does not reflect reality.

5. Women Are Less Educated Than Men

argumentative essay on gender pay gap

Another argument seeking to debunk the discrimination aspect of the gender wage gap is the notion that men are supposedly more highly educated than women. In fact, reality reflects quite the opposite. According to MONEY , in the United States, women are actually more highly educated than men and are graduating from college and graduate school at a higher rate.

However, frustratingly, women's educational degrees are seemingly worth less than men's. MONEY cited a study from Georgetown University that showed that "men with some college but no degree earn about the same as women with a Bachelor’s degree ...and that women have to have a Ph.D. to make as much as men with a B.A."

Thus, the gender pay gap exists because women's education and experience is valued much less than men's—not because women are less qualified than men.

6. Women Are Not Primary Wage Earners

argumentative essay on gender pay gap

Critics of the gender wage gap sometimes assert that women are not primary wage earners in their households and that gender disparities in pay reflect this notion. However, this is, again, misguided criticism for many reasons. First, according to FiveThirtyEight, among heterosexual married American couples, almost 40 percent of women are the primary breadwinners . Furthermore, according to the New York Times, more than 75 percent of single mothers are the sole breadwinner for their household.

Beyond the fact that women are now financially contributing to their households more than ever before, the primary wage earner argument also does not hold up simply because it should not excuse disparities in pay for equal work.

If men and women are working the same job for the same amount of hours, they should paid equally, regardless of whether or not they are the primary breadwinners in their households.

7. Women Engage In More Part-Time Work

argumentative essay on gender pay gap

Another argument that seeks to diminish the significance of the gender pay gap is the notion that women engage in more part time work and therefore earn less than men. However, according to The Guardian, the issue of part-time versus full-time work is having increasingly less impact on the gender wage gap.

In 2016, The Guardian measured the impact of part-time work on the wage gap and found that its impact had declined from 14 to only 4 percent , mostly because of more flexible telework options and the emergence of more higher paying part-time jobs overall.

Moreover, the newspaper also pointed out that responsibilities that women are still expected to disproportionately fulfill at home (like chores and childcare) often contribute to their decision to engage in part-time work, calling into question whether not working full-time actually reflects a choice women make or an expectation for their roles in society and their households.

8. Equal Pay Requirements Are Unjust Or Anti-Capitalist

Critics of equal pay legislation have also argued that making equal pay legally mandatory amounts to what they believe is "socialism" and does not allow space for merit-based pay. Indeed, in 2015 Donald Trump echoed this sentiment, saying, "If you start getting involved with government on ‘this one gets this pay and this one gets that pay,’ and then you say — ‘Where does it all start?’ ...if you sort of say, ‘everybody gets equal pay,’ you get away from the whole American Dream. You get away from capitalism in a sense."

Of course, believing that equal pay for equal work equates to socialism and inhibits merit-based pay is essentially implying that women are inherently worse at their jobs than men, otherwise the gender pay gap would be closed. This is obviously completely untrue, for all of the reasons described above.

Furthermore, saying that equal pay legislation is anti-capitalistic implies a complete lack of belief in the existence of a discriminatory gender pay gap, which has been repeatedly confirmed through many studies and analyses .

9. The Wage Gap Is Inaccurate Or Doesn't Exist

Finally, many critics also argue that the gender wage gap does not reflect reality and cannot take into account all of the factors that might contribute to a disparity in pay between men and women. However, beyond just calculating an average gender wage gap, many "like-for-like" studies have been conducted comparing male and female compensation in the same roles and ultimately demonstrate that women are paid less. Furthermore, the aforementioned Guardian study also demonstrated that even when taking into account a myriad of factors that could affect the wage gap, such as time off, industry, part-time vs. full time work, etc, it was still found that the primary cause for the gender wage disparity was sex discrimination.

Overall, there exists a variety of evidence to debunk some of the most common arguments people use to discredit the gender pay gap. Hopefully Equal Pay Day can serve as an opportunity to educate people about the inaccuracy of these arguments and help garner even more support for wage equality.

argumentative essay on gender pay gap

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argumentative essay on gender pay gap

The Enduring Grip of the Gender Pay Gap

Table of contents.

The gender pay gap – the difference between the earnings of men and women – has barely closed in the United States in the past two decades. In 2022, American women typically earned 82 cents for every dollar earned by men. That was about the same as in 2002, when they earned 80 cents to the dollar. The slow pace at which the gender pay gap has narrowed this century contrasts sharply with the progress in the preceding two decades: In 1982, women earned just 65 cents to each dollar earned by men.

Line chart showing gender pay gap narrowed in the 1980s and ’90s, but progress has stalled since

There is no single explanation for why progress toward narrowing the pay gap has all but stalled in the 21st century. Women generally begin their careers closer to wage parity with men, but they lose ground as they age and progress through their work lives, a pattern that has remained consistent over time. The pay gap persists even though women today are more likely than men to have graduated from college. In fact, the pay gap between college-educated women and men is not any narrower than the one between women and men who do not have a college degree. This points to the dominant role of other factors that still set women back or give men an advantage.

One of these factors is parenthood. Mothers ages 25 to 44 are less likely to be in the labor force than women of the same age who do not have children at home, and they tend to work fewer hours each week when employed. This can reduce the earnings of some mothers, although evidence suggests the effect is either modest overall or short-lived for many. On the other hand, fathers are more likely to be in the labor force – and to work more hours each week – than men without children at home. This is linked to an increase in the pay of fathers – a phenomenon referred to as the “ fatherhood wage premium ” – and tends to widen the gender pay gap.

Related: Gender pay gap in U.S. hasn’t changed much in two decades

Family needs can also influence the types of jobs women and men pursue , contributing to gender segregation across occupations. Differential treatment of women, including gender stereotypes and discrimination , may also play a role. And the gender wage gap varies widely by race and ethnicity.

Pew Research Center conducted this study to better understand how women’s pay compared with men’s pay in the U.S. in the economic aftermath of the COVID-19 outbreak .

The study is based on the analysis of monthly Current Population Survey (CPS) data from January 1982 to December 2022 monthly files ( IPUMS ). The CPS is the U.S. government’s official source for monthly estimates of unemployment . For a quarter of the sample each month, the CPS also records data on usual hourly earnings for hourly workers and usual weekly earnings and hours worked for other workers. In this report, monthly CPS files were combined to create annual files to boost sample sizes and to analyze the gender pay gap in greater detail.

The comparison between women’s and men’s pay is based on their median hourly earnings. For workers who are not hourly workers, hourly earnings were computed as the ratio of usual weekly earnings to usual weekly hours worked. The samples include employed workers ages 16 and older with positive earnings, working full time or part time, including those for whom earnings were imputed by the Census Bureau . Self-employed workers are excluded because their earnings are not recorded in the CPS.

The COVID-19 outbreak affected data collection efforts by the U.S. government in its surveys, especially in 2020 and 2021, limiting in-person data collection and affecting the response rate. It is possible that some measures of economic outcomes and how they vary across demographic groups are affected by these changes in data collection.

“Mothers” and “fathers” refer to women and men 16 and older who have an own child younger than 18 living in the household.

The U.S. labor force, used interchangeably with the workforce in this analysis, consists of people 16 and older who are either employed or actively looking for work.

White, Black and Asian workers include those who report being only one race and who are not Hispanic. Hispanics are of any race. Asian workers include Pacific Islanders. Other racial and ethnic groups are included in all totals but are not shown separately.

“High school graduate” refers to those who have a high school diploma or its equivalent, such as a General Education Development (GED) certificate, and those who had completed 12th grade, but their diploma status was unclear (those who had finished 12th grade but not received a diploma are excluded). “Some college” include workers with an associate degree and those who attended college but did not obtain a degree.

How the gender pay gap increases with age

Younger women – those ages 25 to 34 and early in their work lives – have edged closer to wage parity with men in recent years. Starting in 2007, their earnings have consistently stood at about 90 cents to the dollar or more compared with men of the same age. But even as pay parity might appear in reach for women at the start of their careers, the wage gap tends to increase as they age.

Line chart showing as women age, their pay relative to the pay of men of the same age decreases

Consider, for example, women who were ages 25 to 34 in 2010. In that year, they earned 92% as much as men their age, compared with 83% for women overall. But by 2022, this group of women, now ages 37 to 46, earned only 84% as much as men of the same age. This pattern repeats itself for groups of women who were ages 25 to 34 in earlier years – say, 2005 or 2000 – and it may well be the future for women entering the workforce now.

Dot plot showing women’s pay relative to men’s drops most sharply around ages 35 to 44

A good share of the increase in the gender pay gap takes place when women are between the ages of 35 and 44. In 2022, women ages 25 to 34 earned about 92% as much as men of the same ages, but women ages 35 to 44 and 45 to 54 earned 83% as much. The ratio dropped to 79% among those ages 55 to 64. This general pattern has not changed in at least four decades.

The increase in the pay gap coincides with the age at which women are more likely to have children under 18 at home. In 2022, 40% of employed women ages 25 to 34 had at least one child at home. The same was true for 66% of women ages 35 to 44 but for fewer – 39% – among women ages 45 to 54. Only 6% of employed women ages 55 to 64 had children at home in 2022.

Similarly, the share of employed men with children at home peaks between the ages of 35 to 44, standing at 58% in 2022. This is also when fathers tend to receive higher pay, even as the pay of employed mothers in same age group is unaffected.

Mothers with children at home tend to be less engaged with the workplace, while fathers are more active

Parenthood leads some women to put their careers on hold, whether by choice or necessity, but it has the opposite effect among men. In 2022, 70% of mothers ages 25 to 34 had a job or were looking for one, compared with 84% of women of the same age without children at home. This amounted to the withdrawal of 1.4 million younger mothers from the workforce. Moreover, when they are employed, younger mothers tend to put in a shorter workweek – by two hours per week, on average – than other women their age. Reduced engagement with the workplace among younger mothers is also a long-running phenomenon.

Dot plot showing younger mothers are less active in the workplace than women without kids at home; fathers are more active

Fathers, however, are more likely to hold a job or be looking for one than men who don’t have children at home, and this is true throughout the prime of their working years , from ages 25 to 54. Among those who do have a job, fathers also work a bit more each week, on average, than men who do not have children at home.

Dot plit showing mothers work fewer hours at jobs than women without kids at home; fathers work more

As a result, the gender gap in workplace activity is greater among those who have children at home than among those who do not. For example, among those ages 35 to 44, 94% of fathers are active in the workforce, compared with 75% of mothers – a gap of 19 percentage points. But among those with no children at home in this age group, 84% of men and 78% of women are active in the workforce – a gap of 6 points.

These patterns contribute to the gap in workplace activity between men and women overall. As of 2022, 68% of men ages 16 and older – with or without children at home – are either employed or seeking employment. That compares with 57% of women, a difference of 11 percentage points. This gap was as wide as 24 points in 1982, but it narrowed to 14 points by 2002. Men overall also worked about three hours more per week at a job than women in 2022, on average, down from a gap of about six hours per week in 1982.

Employed mothers earn about the same as similarly educated women without children at home; both groups earn less than fathers

Parenthood affects the hourly earnings of employed women and men in unexpected ways. While employed mothers overall appear to earn less than employed women without children at home, the gap is driven mainly by differences in educational attainment between the two groups. Among women with similar levels of education, there is little gap in the earnings of mothers and non-mothers. However, fathers earn more than other workers, including other men without children at home, regardless of education level. This phenomenon – known as the fatherhood wage premium – is one of the main ways that parenthood affects the gender pay gap among employed workers.

argumentative essay on gender pay gap

Among employed men and women, the impact of parenting is felt most among those ages 25 to 54, when they are most likely to have children under 18 at home. In 2022, mothers ages 25 to 34 earned 85% as much as fathers that age, but women without children at home earned 97% as much as fathers. In contrast, employed women ages 35 to 44 – with or without children – both earned about 80% as much as fathers. The table turns for women ages 45 to 54, with mothers earning more than women with no children at home. Among those ages 35 to 44 or 45 to 54, men without children earned only 84% as much as fathers.

Bar chart showing others earn about as much as women with no children at home who have the same level of education

When the earnings of mothers are compared with those of women without children at home who have the same level of education, the differences either narrow or go away. Among employed women ages 25 to 34 with at least a bachelor’s degree, both mothers and women without children at home earned 80% as much as fathers in 2022. Among women ages 25 to 34 with a high school diploma and no further education, mothers earned 79% as much as fathers and women with no children at home earned 84% as much. The narrowing of the gap in earnings of mothers and women without children at home after controlling for education level also extends to other age groups.

Thus, among the employed, the effect of parenthood on the gender pay gap does not seem to be driven by a decrease in mothers’ earnings relative to women without children at home. Instead, the widening of the pay gap with parenthood appears to be driven more by an increase in the earnings of fathers. Fathers ages 25 to 54 not only earn more than mothers the same age, they also earn more than men with no children at home. Nonetheless, men without children at home still earn more than women with or without children at home.

Although there is little gap in the earnings of employed mothers and women with no children at home who have the same level of education, there is a lingering gap in workplace engagement between the two groups. Whether they had at least a bachelor’s degree or were high school graduates, mothers ages 25 to 34 are less likely to hold a job or be looking for one. Similarly, younger mothers on average work fewer hours than women without children at home each week, regardless of their education level. The opposite is true for fathers compared with men without children at home.

Progress in closing the gender pay gap has slowed despite gains in women’s education

Line chart showing women are more likely than men to hold at least a bachelor’s degree

The share of women with at least a bachelor’s degree has increased steadily since 1982 – and faster than among men. In 1982, 20% of employed women ages 25 and older had a bachelor’s degree or higher level of education, compared with 26% of employed men. By 2022, 48% of employed women had at least a bachelor’s degree, compared with 41% of men. Still, women did not see the pay gap close to the same extent from 2002 to 2022 as they did from 1982 to 2002.

In part, this may be linked to how the gains from going to college have changed in recent decades, for women and men alike. The college wage premium – the boost in earnings workers get from a college degree – increased rapidly during the 1980s. But the rise in the premium slowed down over time and came to a halt around 2010. This likely reduced the relative growth in the earnings of women.

Dot plot showing women with a bachelor’s degree face about the same pay gap as other women

Although gains in education have raised the average earnings of women and have narrowed the gender pay gap overall, college-educated women are no closer to wage parity with their male counterparts than other women. In 2022, women with at least a bachelor’s degree earned 79% as much as men who were college graduates, and women who were high school graduates earned 81% as much as men with the same level of education. This underscores the challenges faced by women of all education levels in closing the pay gap.

Notably, the gender wage gap has closed more among workers without a four-year college degree than among those who do have a bachelor’s degree or more education. For example, the wage gap for women without a high school diploma narrowed from 62% in 1982 to 83% in 2022 relative to men at the same education level. But it closed only from 69% to 79% among bachelor’s degree holders over the same period. This is because only men with at least a bachelor’s degree experienced positive wage growth from 1982 to 2022; all other men saw their real wages decrease. Meanwhile, the real earnings of women increased regardless of their level of education.

Dot plot showing women and men tend to work in different occupations, but some differences have narrowed since 1982

As women have improved their level of education in recent decades, they’ve also increased their share of employment in higher-paying occupations, such as managerial, business and finance, legal, and computer, science and engineering (STEM) occupations. In 1982, women accounted for only 26% of employment in managerial occupations. By 2022, their share had risen to 40%. Women also substantially increased their presence in social, arts and media occupations. Over the same period, the shares of women in several lower-paying fields, such as administrative support jobs and food preparation and serving occupations, fell significantly.

Even so, women are still underrepresented in managerial and STEM occupations – along with construction, repair and production, and transportation occupations – when compared with their share of employment overall. And there has been virtually no change in the degree to which women are over represented in education, health care, and personal care and services occupations – the last of which are lower paying than the average across all occupations. The distribution of women and men across occupations remains one of the drivers of the gender pay gap . But the degree to which this distribution is the result of personal choices or gender stereotypes is not entirely clear.

Gender pay gap differs widely by race and ethnicity

Looking across racial and ethnic groups, a wide gulf separates the earnings of Black and Hispanic women from the earnings of White men. 3 In 2022, Black women earned 70% as much as White men and Hispanic women earned only 65% as much. The ratio for White women stood at 83%, about the same as the earnings gap overall, while Asian women were closer to parity with White men, making 93% as much.

Dot plot showing Black and Hispanic women experience the largest gender wage gap

The pay gap narrowed for all groups of women from 1982 to 2022, but more so for White women than for Black and Hispanic women. The earnings gap for Asian women narrowed by about 17 percentage points from 2002 to 2022, but data for this group is not available for 1982.

To some extent, the gender wage gap varies by race and ethnicity because of differences in education, experience, occupation and other factors that drive the gender wage gap for women overall. But researchers have uncovered new evidence of hiring discrimination against various racial and ethnic groups, along with discrimination against other groups, such as LGBTQ and disabled workers. Discrimination in hiring may feed into differences in earnings by shutting out workers from opportunities.

Broader economic forces may impact men’s and women’s earnings in different ways

Changes in the gender pay gap are also shaped by economic factors that sometimes drive men’s and women’s earnings in distinctive ways. Because men and women tend to work in different types of jobs and industries, their earnings may respond differently to external pressures.

Line chart showing the growth in women’s earnings has slowed in the past two decades

More specifically, men’s earnings essentially didn’t change from 1982 to 2002. Potential reasons for that include a more rapid decline in union membership among men, a shift away from jobs calling for more physical skills, and global competition that sharply reduced employment in manufacturing in the 1980s. At the same time, women’s earnings increased substantially as they raised their level of education and shifted toward higher-paying occupations.

But in some ways, the economic climate has proved less favorable for women this century. For reasons that are not entirely clear, women’s employment was slower to recover from the Great Recession of 2007-2009. More recently, the COVID-19 recession took on the moniker “ she-cession ” because of the pressure on jobs disproportionately held by women . Amid a broader slowdown in earnings growth from 2000 to 2015, the increase in women’s earnings from 2002 to 2022 was not much greater than the increase in men’s earnings, limiting the closure in the gender pay gap over the period.

What’s next for the gender pay gap?

Higher education, a shift to higher-paying occupations and more labor market experience have helped women narrow the gender pay gap since 1982. But even as women have continued to outpace men in educational attainment, the pay gap has been stuck in a holding pattern since 2002, ranging from 80 to 85 cents to the dollar.

More sustained progress in closing the pay gap may depend on deeper changes in societal and cultural norms and in workplace flexibility that affect how men and women balance their careers and family lives . Even in countries that have taken the lead in implementing family-friendly policies, such as Denmark, parenthood continues to drive a significant wedge in the earnings of men and women. New research suggests that family-friendly policies in the U.S. may be keeping the pay gap from closing. Gender stereotypes and discrimination, though difficult to quantify, also appear to be among the “last-mile” hurdles impeding further progress.

argumentative essay on gender pay gap

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  • It is also worth noting that even if the hourly earnings of mothers are not affected, their weekly or annual pay is reduced in line with the reduction in the hours worked. ↩
  • In part, this is because the age of women at first birth varies by educational attainment . Motherhood among women with a bachelor’s degree or higher level of education occurs at an older age than among women without a bachelor’s degree. ↩
  • Although Asian men earned about 24% more than White men at the median in 2022, comparisons in this section are drawn with reference to White men. In 2022, White men accounted for about one-third of total employment in the U.S., compared with about 3% for Asian men. ↩

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The Gender Pay Gap and the Equality

This essay will discuss the gender pay gap and its implications for gender equality. It will explore the causes of the pay gap, including discrimination, occupational segregation, and societal norms. The piece will also consider the economic and social consequences of the pay gap and the measures being taken to address this inequality, such as legislation, corporate policies, and advocacy efforts. You can also find more related free essay samples at PapersOwl about Discrimination.

How it works

  • 1 Introduction
  • 2 Pro Arguments
  • 3 Con Arguments
  • 4 Conclusion

Introduction

The gender pay gap and the equality of pay rates have always been topics of discussion in today’s society. Equal pay means that individuals accomplishing the same work should be compensated equivalently in regards to completion. Issues are raised between the earning differences between men and women due to the lack of equal pay between the two genders. By referring back to U.S. history on the subject, we found that the issue dates back over 100 years ago and grasps the concept around competencies and skills ultimately undervalued from women whose jobs are paid less than that of men, as a result, contradicting their effort in volume of production at work while also hindering their chances of potential growth in an organization.

The importance of unfair pay can be a detrimental factor when it comes to sustaining income necessary for present and future survival. Our position argues that the gender pay gap is not a myth, and must be taken seriously in order to ensure everyone has an equal chance of being recognized for the hard work they put into society.

Pro Arguments

One of the main elements to the gender pay gap being real is gender discrimination. Gender discrimination can result from women being excluded from meetings, promotions, and not being offered a job because the employer believes women are not as smart or determined enough as men in the workplace. As mentioned by Hessaramiri and Kleiner, male decision makers believe that men are superior, so they make decisions with that thought in mind (2001, p.44). Employers who do not hire women because they believe that a man will perform more efficiently is discriminating against women by stereotyping them. As discussed in The Complex Causes of the Gender Pay Gap, when employers are presented with identical resumes for male and female candidates, employers will more likely end up offering the job to a male (Wagner, 2015, p.17). Having identical qualifications to men, women are not considered at a man’s level, which is discrimination. Donna Kassman was overlooked for a promotion even though she was more than qualified (Widmer, p.23).

Unequal career advancement widens the pay gap for both females and males. According to a recent student founded by SHRM, women earn 0.79 cents for every dollar a man earns (Miller, S). These statistics have been unchanged for several years considering the similarities of jobs and qualifications. This highlights the underlying issue of gender pay gap that has still yet to be brought into major topic discussions to be acted upon. In addition, studies show 36% of men felt that women should not earn the same amount as they do if able to take family leave. However, this is a somewhat arguable point since there is indeed a discriminatory act towards pay differences due to personal matter. This could be a personal opinion looking into the negative sides of women taking time off to care for a child and not focusing on their work. The differences in how men and women are seen towards one another sets the stage for even further pressures in the workplace.

54% of women experience harassment in the workplace (Zetlin, 2018). Due to these pressures women receive from men, the concept of a wage gap between the two genders further defines itself. Women decide to leave these “male-dominated” industries to protect their values and themselves, but in turn hurt their chances of promotions and career advancement. The “leaving” of women in these jobs is more of them being pushed out of these roles due to their refusal of complying with their male co-workers’ ideas. According to The Cut, “Sexual harassment has been identified as one of the most damaging and ubiquitous barriers to career success and satisfaction for women,” (Covert, 2017, para. 2). Since the harassment of women is so common in the workplace, females experience lower incomes because they are shut out from certain promotions. This causes women to face even more obstacles when finding a job, thus earning less than their male co-workers.

Con Arguments

Women are willingly choosing low paying job as a result to the difference in pay wage. When speaking about the wage gap, people often tend to look at the same job with same skills to compare the differences each gender is getting pay for. Often times, this leads to findings that women are getting paid less than men per hour or annually for the same/similar job. However, research has shown that women are more likely to choose a career field that will result in lower pay for their earnings in total. Based on research by Harvard Business Review, “Many college majors that lead to high-paying roles in tech and engineering are male dominated, while majors that lead to lower-paying roles in social sciences and liberal arts tend to be female dominated, placing men in higher-paying career pathways, on average” (Carmichael, S). Yes, this is also a contributing factor adding up to wage gap. Overall, data does not account for the reason why women pick a career that lead to a lower pay but all comes down to their behaviors and choices leading up to the outcomes of their career. The argument here is that both women and men can be in the same major but it is the personal characteristics and determination that establish an outcome, or career, they want to achieve.

Women bringing the pay gap upon themselves by choosing to go into lower-paying jobs is a myth. Even though a large percentage of women are employed in lower paying jobs, their difference in industry choice compared to men still depicts a gap in wages per occupation, favoring the male gender. Regardless of the industry, women consistently earn less than their male colleagues. For example, it is fair to say that society views nursing and secretarial jobs as a more female-dominated industry; however, studies from Fortune Magazine depict that women still earn less than men in these fields. To further the argument, there is evidence that in most occupations across the board, wages fall when more women are present in the industry and in turn, the wages rise when there are more males present in the industry. In response to these unfair wages in the workplace, women ask for pay raises but only receive them 15% of the time while males receive their pay raises 20% of the time (Vagins, 2019) A reason for this is that females asking for a higher salary are seen as aggressive and are thus refused their request.

It is believed the gender pay gap is a myth considering the hours worked by both men and women, women make the choice to not work as many hours as men. Working overtime is not exclusive to only one gender, so when men decide to work more overtime, of course men will make more money than women. In a study by the Bureau of Labor Statistics, 27 percent of men compared to 15 percent of women work over 41 hours (Hymowitz, 2011, p.4). There is more than enough evidence to claim that women prefer to work fewer hours than men. As stated in Why the Gender Gap Won’t Go Away, despite the gap in earnings, women still decide to work fewer hours than men (Hymowitz, 2011). If women want the pay gap to be closed, all they must simply do is work the same hours as men. Their choice ends up causing the wage gap to show up with skewed results. There is a problem with this argument. Women do have a choice to work the hours they want, but the lack of work-family life balance programs lead women to choose lower paying jobs with more flexible hours.

Some believe the pay gap is due to women having children and leaving work to look after them. This myth is false because right off the bat, the gap in wages is present even before women create families, indicating that a distinction between male and female wages is already established. Fortune Magazine depicts that women before the age of 24 already earn less than their male co-workers; thus, proving that the differences in wages are present even before the main “child bearing” years experienced by women. When the hiring process is underworks, pregnant women are seen differently than men. Mothers-to-be as well as women already with children experience discrimination from employers and are “six times less likely” to be considered for a job when compared to childless women and men (Dunn, 2019, para. 11). While women are seen as less reliable to meet deadlines and scheduled appointments than men because of their “motherhood” tasks, men are seen as needing to be rewarded for having a new child and thus receive “daddy bonuses” to help their family out (Miller, 2018). SHRM explains that women who take a mid-career break from work earn less than men who also take similar mid-career breaks.

During our research to reveal that the gender pay gap is a myth, we found reasonable evidence to fortify our statements. Our analytical research proves that the lack of equal pay in society is indeed still in question by government entities as well as small sized companies to large sized corporations from across all job industries. Behavioral and work-life balance contributes to women earning less than men since it is found that they tend to work less according to the Bureau of Labor Statistics. We can conclude that personal factors such as lower paying jobs within respective career choices and degrees leads women to earn less. Even in so-called women dominated field, the research found relatively disproportionate annual pay between men and women. With the given evidence in our report, there should be a dramatic change in how equal pay is approached as well as the issue of the gender pay gap.”

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The root of WNBA players' argument about the gender wage gap

The Minnesota Lynx's Maya Moore dribbles past the Dallas Wings' Kayla Thornton, left, and Liz Cambage at Target Center in Minneapolis on Tuesday, June 19, 2018. The Lynx won, 91-83. (Jerry Holt/Minneapolis Star Tribune/TNS)

Credit: Jerry Holt

As the Dallas Wings sat at Dallas-Fort Worth Airport on June 18, waiting for their flight to Minnesota to face the Lynx, a conversation with her teammates spurred center Liz Cambage to take to Twitter.

"Today I learnt NBA refs make more than a WNBA player and the 12th man on a NBA makes more than a WHOLE WNBA team," she tweeted. "Not sure if I want a sex change or a career change right now.

"It's (quite) frustrating going to bed most nights knowing that if I was born with a penis I would be entitled to so much more in life. But ... I love being a woman I will never stop fighting for my sisters."

The conversation around the gender pay disparity in basketball has picked up steam in recent months. It mirrors a similar societal movement as women demand equal treatment and compensation in the workplace.

As Cambage pointed out, while a WNBA player's maximum salary is $115,000 for those who have been in the league six years, entry-level NBA referees earn $150,000. The average salary for WNBA players is around $79,000.

"I don't know how much traction we're really getting with these conversations," said Wings guard Skylar Diggins-Smith, who has been a vocal advocate for equal pay for women in basketball. "They may be coming up and causing conversations but what's actually happening (to change), I don't know the numbers."

The important thing to note here is that WNBA players are not asking for $200 million contracts to match their male counterparts in the NBA. They understand that a variety of factors, from the NBA's larger schedule to television deals and sponsorships, makes that an unreasonable position to take.

Instead, the argument comes down to the cut of league revenue that WNBA players receive.

Forbes studied available data for WNBA revenue in 2017. This included the $25 million broadcast deal with ESPN and a minimum of $26.5 million from the gate, using only the minimum ticket prices to determine this figure.

While the WNBA brought in at least $51.5 million, the players _ with their average salaries of $75,000 _ earned $11,775,000 combined.

That comes out to 22.8 percent of league revenue for the players. NBA players earn 49-51 percent of league revenue annually.

That 22.8 percent is the maximum estimate of what WNBA players earn, too. Because the league does not disclose its annual revenue, Forbes speculates that it is very likely that players receive an even lower cut.

"I was even surprised hearing that number. That's information I just found out, being in the league six years," Diggins-Smith said. "We're not even talking about $200 million salaries or anything like that. It's just off the revenue that we bring in. Getting that same percentage (as NBA players), and not even getting half of that is kind of appalling. It's staggering."

She added, "I think a lot of people try to hijack the narrative that we're trying to talk about. That's a fact. It's not anything we're making up. We've earned everything that we've gotten. We've earned the right to play in this league. It's not like, 'Just be grateful that you're just in it.' "

The issue could come to a head following the 2019 season. That's when the Women's National Basketball Players Association (WNBPA) can opt out of the current collective bargaining agreement and begin negotiating a new one.

"All 144 players have a seat at the table at this next collective bargaining agreement," Diggins-Smith said. "I think it's just about us prioritizing and getting this information out to us in layman's terms and just continuing to be advocates for women, women's rights and gender parity."

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Gender pay gap persists and women are taxed for remote work, study finds.

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The gender pay gap isn’t closing; women are penalized for becoming parents while men aren’t; and ... [+] working remotely carries an additional pay tax for women.

The gender pay gap isn’t closing; mothers are penalized for becoming parents while fathers are rewarded; and working remotely carries an additional pay tax for women. These are just some of the conclusions reached in a major new report which draws on pay data from over 600,000 U.S. workers.

Compiled by Payscale Inc, a provider of compensation data, software and services, the report found that, on average, women earn 83 cents for every dollar taken home by a comparable man which, despite new pay legislation and national efforts to clamp down on inequality, remains virtually unchanged from a year ago.

Working mothers, the report found, earn just 75 cents compared to working fathers, and the gender pay gap widens as women progress in their careers. It stands at 87 cents when they enter the workforce but reaches, on average, 82 cents by the time a woman is 30 to 40 years old, and 74 cents by the time she is 45.

According to the data, the pay gap is 10 cents wider for women who work remotely—at 79 cents—compared to women who work in-person. Payscale also found that working fathers actually earn, on average, 15% more than men without children implying a fatherhood bonus compared to a motherhood penalty, as it’s often referred to.

In its calculations, Payscale distinguishes between the controlled and uncontrolled pay gaps. The former refers to a comparison between women and men doing exactly the same job with the same qualifications—what’s commonly referred to as “equal pay for equal work.” By that metric, Payscale found, the gap has almost closed with women making 99 percent of what their male counterparts earn. The uncontrolled gap, however, remains entrenched and largely unmovable despite policy efforts, especially in the form of pay transparency laws in states like California, Connecticut and New York.

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“Pay transparency laws present a unique and distinct advantage for those entering the job market, especially to those affected by pay gaps such as women. Candidates now have access to salaries on job postings before applying, which gives them an understanding about how to negotiate their compensation. The laws also empower women to seek higher paying roles, helping to end a long cycle of inequality,” said Lulu Seikaly, senior corporate employment attorney at Payscale. “We are still in the early days of pay transparency legislation, but as these laws roll out globally, we hope to start seeing a significant impact soon.”

One marker of progress in the report is that, according to the data, the controlled gender pay gap has closed in 2024 for American Indian and Alaska Native, Asian, Black, and Hispanic women. Those groups have also experienced some progress in terms of the uncontrolled pay gap. Since 2019, the uncontrolled gender pay gap has closed by 5 cents for Black, American Indian and Alaskan Native women, to 80 cents, 74 cents and 74 cents respectively, and by 4 cents for Hispanic, Native Hawaiian and Other Pacific Islander women, to 79 cents, 80 cents and 80 cents, respectively.

Over that same period, the uncontrolled gender pay gap for white women has closed by just 2 cents to 83 cents and by just 1 cent for Asian women, to 96 cents.

Josie Cox

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Home / Essay Samples / Social Issues / Gender Wage Gap / Breaking the Gender Barrier in Sports: Addressing the Pay Gap

Breaking the Gender Barrier in Sports: Addressing the Pay Gap

  • Category: Social Issues
  • Topic: Gender Discrimination , Gender Wage Gap

Pages: 7 (3123 words)

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  • Jessica Hills, 2019. An in-depth look at the gender wage gap issue in sports. Chicago, Illinois: peak. Retrieved from: https://the-peak.ca/2019/02/an-in-depth-look-at-the-gender-wage-gap-issue-in-sports/
  • Kristen Crowdy, 2019. The Fight for Equal Pay in Women’s Sports. New York, NY: Womens Sports Foundation. Retrieved from: https://www.womenssportsfoundation.org/education/fight-equal-pay-womens-sports/
  • Andrew Das. 2019. Jill Ellis Will Step Down As U.S. Women’s Coach. New York, NY: NY Times. Retrieved from: https://www.nytimes.com/2019/07/30/sports/soccer/jill-ellis-uswnt.html
  • ESPN Associated Press. 2019. Fans Fete USWNT in NYC, join equal pay crusade. NY, NY: ESPN Retrieved from: https://www.espn.com/soccer/fifa-womens-world-cup/story/3897845/fans-fete-uswnt-in-nycjoin-equal-pay-crusade
  • Mark Dodds. 2019. Women: Equal Pay. Washington DC, Washington: NY Times. Retrieved from: https://search-credoreference-com.proxy195.nclive.org/content/entry/mbmsports/women_equal_pay/0
  • Kim Kelly. 2019. Pay Discrimination in Women’s Sports Is a Labor Issue. New York, NY: Teen Vogue. Retrieved from: https://www.teenvogue.com/story/pay-discrimination-women-sports-labor-issue
  • Daniel Moritz-Rabson. 2019. Gender Pay Gap in Sports is More of an Issue After Women’s World Cup Win. New York, NY: Newsweek Retrieved from: https://www.newsweek.com/gender-pay-gap-sports-more-issue-after-womens-world-cup-win-1451335

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